Multimodal Federated Learning

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 34

Oct 7, 2026cs.CV

FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification

Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these approaches rely on aligning these domains using the cosine similarity metric, which fails to capture token-level structure and cross-modal interactions prior to the classification stage. This is especially critical in biomedical applications under federated constraints, where data sharing is restricted, labeled data is scarce at each site, and it differs widely across institutions, leading to substantial statistical heterogeneity. To overcome this issue, we propose FedSSMCoOp, a federated few-shot image classification framework that enables multimodal learning while preserving data privacy. With the help of the SSM-based Vision Mamba and Cross Mamba blocks, and by optimizing only the soft-prompt and communication-prompt updates in the federated setting, the framework prioritizes both computation and performance. Importantly, this eliminates the need to use an external Large Language Model (LLM) for feature alignment. The framework is further trained and evaluated on various biomedical image datasets, and its performance is assessed. The proposed framework delivers stable performance relative to the baselines and is, on average, 1.96 times lighter. The corresponding script will be made available soon.

Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya +1

Department of Computer Science and Engineering Indian Institute of Technology Hyderabad · Department of Artificial Intelligence Indian Institute of Technology Hyderabad · Department of Electrical Engineering Indian Institute of Technology Hyderabad

Few-Shot Image Classification Multimodal Federated Learning Federated Learning Medical Image Classification

Sep 30, 2026cs.AI

Prototype-guided Bilateral Alignment Multimodal Federated Learning

Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing methods predominantly rely on idealized assumptions of model homogeneity and balanced modality distributions, rendering them ill-suited for practical scenarios characterized by heterogeneous client architectures and severe modality imbalance. To address these challenges, we propose a \textbf{M}ultimodal \textbf{Fed}erated learning Prototype-guided Bilateral Alignment (MFedPBA) framework. MFedPBA facilitates robust knowledge synergy through a dual alignment mechanism: (i) at the feature level, it aligns heterogeneous feature spaces via a projection encoder optimized by contrastive learning and the Gromov-Wasserstein distance; (ii) at the decision level, it employs an entropy-weighted aggregation of naturally aligned logit prototypes. This novel design achieves robust MFL by jointly tackling heterogeneous feature spaces and collectively aggregating decisions. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines under conditions of model heterogeneity and modality imbalance.

Tianchi Liao Tianchi_Liao, Lele Fu, Sheng Huang +3

Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China · School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China

Multimodal Federated Learning Federated Learning Aggregation Heterogeneous Federated Learning

Sep 27, 2026cs.AI

Federated Multi-Modal Human Activity Recognition using Multi-Agent Reinforcement Learning

Human Activity Recognition (HAR) from heterogeneous wearable sensors is fundamental to the Internet of Health Things (IoHT), supporting rehabilitation, elderly care, and smart healthcare. Existing multimodal fusion methods often assign fixed equal weights to sensor streams, overlooking differences in modality importance, acquisition cost, and sensor quality, which can vary due to movement, incorrect placement, or temporary blockage. We propose an adaptive and cost-aware multimodal HAR framework based on multi-agent reinforcement learning for centralized HAR and extend it to federated learning as FedMHAR. In the centralized setting, multimodal fusion is formulated as a cooperative Multi-Agent Reinforcement Learning (MARL) problem, where each sensing modality is assigned a PPO-based agent that learns per-sample fusion weights, enabling the model to emphasize informative modalities while down-weighting costly sensors when cheaper alternatives provide sufficient information. In the federated setting, we introduce BiFL-PPO, a bidirectional federated optimization strategy in which a server-side PPO policy learns client-specific trust weights and feeds them back to adapt local learning rates and proximal regularization. Unlike round-level optimization, BiFL-PPO uses dense batch-level rewards for more frequent feedback and stable training under heterogeneous client data. Evaluation on the MEx Rehabilitation and UTD Multimodal Human Action datasets shows that the centralized framework achieves 87.30% and 94.98% accuracy, respectively, outperforming conventional fusion methods and state-of-the-art HAR models. FedMHAR achieves 79.74% and 77.49% in the federated setting, consistently surpassing FedAvg, FedProx, FedBN, FedNova, and AdaFedProx, while providing more stable performance and reducing sensor acquisition cost.

Debasmita Dey, Tanmay Sen, Himel Mallick

SQC & OR Unit Indian Statistical Institute Kolkata Kolkata, India · Department of Population Health Sciences Cornell University 425 E 61st Street New York

Multimodal Federated Learning Human Activity Recognition Cooperative MARL Multimodal Sensor Fusion

Sep 17, 2026cs.LG

QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles

Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalized in-cabin services. However, integrating high-fidelity multimodal fusion with collaborative training is often hindered by the heterogeneous and time-varying Quality of Service (QoS) constraints of vehicular networks. Standard Federated Learning (FL) approaches enforce rigid synchronous rounds that fail to account for these resource asymmetries, leading to safety-critical timing violations and energy exhaustion. In this paper, we propose FedQoS, a novel asynchronous, event-triggered FL framework that decouples local computation from global communication via a two-phase gating mechanism. First, we introduce a resource-aware training gate that initializes local learning only when sensing buffers and energy reserves meet safety thresholds, preventing ML tasks from compromising core vehicle mobility. Second, a QoS-aware transmission policy gates uplink updates based on an efficiency score that balances model novelty against instantaneous latency and energy costs. Locally, clients optimize an objective featuring a staleness-aware proximal term that dynamically adjusts the global anchor strength based on update age. Extensive experiments on multimodal vehicular datasets demonstrate that FedQoS achieves competitive personalized accuracy with only marginal performance loss compared to FedAvg, while substantially reducing QoS violations, cutting communication overhead by 76.7%, and lowering latency cost by 26.0%, demonstrating a highly favorable accuracy and efficiency balance for real-world vehicular deployments.

Baran Can Gül, Mert Nakıp, Nasser Jazdi +1

Institute of Industrial Automation and Software Engineering, University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany · Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (IITIS PAN), 44-100 Gliwice, Poland · Ideatrum Ltd. Sti., 35560, İzmir, Türkiye

Multimodal Federated Learning Asynchronous Federated Learning Federated Learning

Sep 10, 2026eess.SP

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and distributed sensing. Vision-language-action (VLA) models offer a foundation by integrating visual perception, language understanding, and action generation into a unified closed-loop policy. However, training and adapting VLA models to distributed robotic agents introduce challenges in privacy protection, communication efficiency, and model heterogeneity. Existing federated learning (FL) methods overlook the intrinsic differences among vision, language, and action pathways in parameter scale, privacy exposure, update dynamics, and tolerance to compression or perturbation. To address this issue, this article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks. FedMVLA incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-sliced transport design that routes the precision-critical action stream through a protected ultra-reliable low-latency slice. A case study on federated robotic manipulation over the Third Generation Partnership Project (3GPP)-based wireless substrate, covering fading, co-channel interference, and malicious jamming, shows that FedMVLA achieves an 84.8% task success rate, exceeds FedAvg by 22.2 percentage points, sustains a widening margin when scaling to 128 clients across eight cells, and reduces the schedule-averaged per-client uplink model-update payload by 95.6% (approximately 96%), while keeping the 95th percentile (p95) of the round-critical uplink completion time near 1.5s.

Zhuodong Liu, Xiangyu Li, Chunhong Yuan +5

Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electronic Science and Technology, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China +4

Multimodal Federated Learning Wireless Communications VLMs for Robotics Federated Learning Heterogeneous Federated Learning

Sep 9, 2026cs.LG

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial void of secure fusion of visual and textual context across distant networks. Thus, we present OmniMed-FL, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly). Our proxy corpus pairs 3,000 public chest radiographs with 3,000 class-conditioned synthetic notes, matched by class, not by patient. The framework benchmarks eight fusion strategies, three initializations, four missing-text imputation rules, and matched federated baselines under non-IID Dirichlet partitioning across 3 to 20 hospital clients. As all notes are synthetic and pairing is not patient-level, these are descriptive proxy comparisons, not estimates of diagnostic performance or deployment readiness. Within those limits with clients (

K=5

) and severe skew (

α=0.1

), local-only training achieves a macro-F1 score of 0.297, FedAvg achieves

0.662\pm0.074

, FedProx

0.737\pm0.085

, a matched FedMME-style one-shot ensemble

0.647\pm0.080

, and our SCAFFOLD-AdamW adaptation

0.070\pm0.015

, the 0.075 FedProx-FedAvg gap falling inside the wider of the two two-seed standard deviations. Over a

4\times3

grid, label skew costs up to 0.27 F1 whereas a near-sevenfold client increase costs at most 0.10, while bidirectional volume grows linearly to 183.5 GiB at

K=20

. Multimodal fusion leads on both corpora, scoring 0.956 against 0.934 for text and 0.664 for images on the synthetic corpus and 0.906 against 0.880 and 0.737 on the radiograph corpus, for

2.3\times

the model state of text alone.

Ayush Debnath, Ruelia Saha, Sudip Misra

Indian Institute of Technology Kharagpur, India · KTH Royal Institute of Technology, Sweden · SRM University-AP, India +1

Multimodal Federated Learning Medical Diagnosis Non-IID Federated Learning Multimodal Classification Multimodal Fusion

Aug 10, 2026cs.LG

Multimodal Federated Learning under Dual-Axis Modality Missingness

Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally. Existing methods typically address these two axes separately. We propose Flux, a multimodal federated learning framework built around two complementary components. First, modality-aware confidence tempering learns sample-specific confidence for each modality through mask-aware unimodal supervision and fuses the confidence estimates from observed modalities into a sample-adaptive temperature that adjusts predictive sharpness according to evidence quality and completeness. Second, gradient-decoupled private adaptation applies this temperature only to a client-private prediction pathway, while training the shared federated model with a standard, untempered objective. This enables sample-specific, client-local confidence adaptation without allowing confidence-dependent gradients to perturb shared representation learning. Across four multimodal datasets, Flux achieves the highest average macro-F1 on every dataset, outperforming the strongest dataset-specific baseline by 0.8~2.2 points and by 1.6 points on average. Additional analyses demonstrate favorable calibration, temperature sensitivity to both modality missingness and input corruption, and more stable shared optimization under private-only tempering. Our code is available at https://github.com/AdibaOrz/Flux.

Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5

1KAIST · 2CUHK · 3HKUST

Multimodal Federated Learning Missing-Modality Learning Probability Calibration Federated Learning Heterogeneous Federated Learning

Aug 1, 2026cs.LG

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains. Federated multimodal graph learning (FMGL) extends federated graph learning (FGL) to MAGs, enabling collaborative optimization across decentralized MAGs without exposing raw data. However, naively applying existing FGL methods to FMGL is insufficient, as they fail to navigate the multifaceted heterogeneity inherent in decentralized MAGs, including task heterogeneity across diverse client objectives, modality heterogeneity from discrepant modality quality and semantic domains, and topology heterogeneity arising from divergent topological patterns with low cross-modality correlation. To address these challenges, we propose Federated multimodal graph learning with Topology-aware Cross-modal Routing (FedTCR), the first systematic algorithm designed for FMGL. To handle task heterogeneity, FedTCR employs a two-stage paradigm that comprises federated task-agnostic pre-training followed by isolated task-oriented fine-tuning. To jointly address modality and topology heterogeneity, FedTCR introduces a topology-aware cross-modal routing mechanism. Concretely, each client distills modality-specific knowledge into compact prototypes via topology-aware importance-weighted aggregation informed by graph structure; the server then evaluates cross-client cross-modal relationships among these structure-informed prototypes and routes informative ones as contrastive references, driving a tri-level cross-modal contrastive learning scheme that jointly aligns cross-client modalities while preserving discrimination. Experiments across 7 domains demonstrate that FedTCR outperforms state-of-the-art baselines on both graph-centric and modality-centric tasks.

Yinlin Zhu, Di Wu, Yi Zhang +5

Sun Yat-sen University, Guangzhou, China1 · Shandong University, Weihai, China2 · Beijing Institute of Technology, Beijing, China3

Multimodal Federated Learning Federated Graph Learning Multimodal Graph Learning Heterogeneous Federated Learning

Jul 30, 2026cs.LG

MMFGU: Multimodal Federated Graph Unlearning

Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data. However, the presence of heterogeneous multimodal content also makes unlearning requests more frequent and fine-grained: users may delete accounts or interactions, remove a particular image or text while retaining the associated entity, or revoke the learned correspondence between retained modalities or graph attributes. Existing federated graph unlearning mainly handles entity/relation or client removal and cannot directly satisfy these multimodal requests. They introduce three challenges: removing only the requested information without damaging retained content, preventing the target from being recovered through remaining modalities or graph neighborhoods, and stopping related traces on other clients from re-entering the global model after aggregation. To address them, we propose \textsc{\textbf{MMFGU}}, a multimodal federated graph unlearning framework built around target-specific representation decoupling. \textsc{MMFGU} maps heterogeneous requests into unified target carriers, decouples requested representations while anchoring retained semantics, exposes and repairs propagated residuals with lightweight probes, and selectively purges affected clients through compact prototype and response signals. Experiments show that \textsc{MMFGU} effectively removes requested information, preserves retained graph utility, and achieves a

\boldsymbol{41.5\times}

speedup over full retraining.

Haodong Lu, Zekai Chen, Weiwei Ji +5

Beijing Institute of Technology Beijing, China

Multimodal Federated Learning Federated Unlearning Federated Graph Learning Multimodal Graph Learning

Jul 30, 2026cs.LG

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging classes from private multimodal graph streams, retain historical categories, and reject samples outside the known class space. In this setting, clients must learn emerging classes from private multimodal graph streams while preserving historical categories and rejecting samples outside the current known class space. The core challenge is catastrophic forgetting, which in federated multimodal graphs is not merely a classifier-level failure: old knowledge can be erased through modality-semantic overwriting, topology-induced structural erosion, and federated memory fragmentation. To address this challenge, we propose \textbf{FedOGL}, a semantic-structural memory preservation framework. On the client side, FedOGL preserves historical decision behavior through replay and task-start distillation, while protecting graph-propagation memory via projection onto a globally shared structure basis. On the server side, FedOGL maintains and transfers compact category prototypes to facilitate cross-client knowledge sharing without exposing raw graph data. Extensive experiments demonstrate that, compared with the best-performing baselines, FedOGL reduces performance degradation caused by catastrophic forgetting by \textbf{42.67%}, while maintaining or improving performance on downstream tasks.

Zekai Chen, Haodong Lu, Shihao Li +5

Beijing Institute of Technology, Beijing, China

Multimodal Federated Learning Federated Continual Learning Federated Graph Learning Multimodal Graph Learning Catastrophic Forgetting

Jul 25, 2026cs.MM

FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities

Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients. Existing methods typically rely on generative imputation, external auxiliary data, or isolated unimodal training to bridge modality gaps, often incurring substantial communication and computational costs as well as potential privacy risks. To address these limitations, we propose FedTaste, a parameter-efficient framework for topology-aware structural transfer in Multimodal Federated Learning with missing modalities. Instead of aligning fragile first-order features, FedTaste focuses on more stable group-level semantic relations. Specifically, FedTaste leverages frozen foundation models to extract a joint multimodal topology from full-modality clients, which is then consolidated by the server into a global structural blueprint. To adapt clients with missing modalities, we introduce Modality-Adaptive Structural Prompts together with spectral consistency regularization, enabling lightweight branch-specific adaptation that aligns local partial representations with the shared blueprint. In this way, FedTaste avoids explicit modality imputation while preserving shared semantic structure across clients. Extensive experiments demonstrate that FedTaste consistently achieves superior performance across multiple datasets and challenging Non-IID settings, while substantially reducing communication overhead compared with existing methods.

Haochen Liang, Jie Zhang, Hideya Ochiai

The University of Tokyo Tokyo, Japan · Great Bay University Dongguan, China

Multimodal Federated Learning Missing-Modality Learning Cross-Modal Representation Learning Non-IID Federated Learning Federated Learning

Jul 21, 2026cs.LG

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning framework using multimodal data, including clinical information, tumour characteristics, biomarker data, and patient demographics, alongside medical imaging data such as MRI scans, to model changes in tumour characteristics over time. The performance of the federated approach was compared with that of a centralised model trained on aggregated data. The report then further examines strategies to enhance secure model updates, maintain performance across patient subgroups, and support scalability across institutions. The findings assess whether federated learning can achieve predictive performance comparable to centralised learning while preserving data locality. These results contribute to understanding the feasibility of privacy-preserving, multimodal predictive modelling and support future applications such as digital twins to assist clinicians and patients in personalised treatment planning.

Ruth Amey, Muhammad Arifur Rahman, Taha Osman +4

Breast Cancer Multimodal Federated Learning Privacy-Preserving ML Clinical Prediction Federated Learning

Jul 17, 2026cs.LG

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs. In practice, such graphs are fragmented across privacy-restricted silos owned by different platforms and institutions, so learning a broadly transferable model over them demands collaborative training that never exposes raw data. This places the task at the intersection of multimodal graph learning and federated learning, yet existing methods cover only one side of it. To address the challenges from these two perspectives, we propose FedGAMMA, casting federated multimodal graph foundation learning as a two-stage semantic-structural alignment problem of federated pre-training and prompt-based fine-tuning. During pre-training, a shared-private semantic enhancer disentangles cross-modal commonality from modality-specific information, aligning it through optimal transport, a topology-aware graph fusion module decouples semantic and structural views via semantic residual graphs and dual positional encodings, and a dual-channel affinity-aware aggregation mechanism estimates client similarity from feature and graph centroids without exposing raw data. During fine-tuning, FedGAMMA adapts the pretrained encoder through lightweight graph-aware prompts, a shared prompt pool with controlled exploration, and channel-wise prompt synchronization. Experiments on twelve multimodal graph datasets show FedGAMMA consistently surpassing a broad range of baselines across downstream tasks, with gains of up to 12.96%. FedGAMMA further outperforms competitive baselines accross multi-domain datasets on multiple tasks with up to 5.71% under few-shot learning scenario.

Xunkai Li, Guohao Fu, Yuming Ai +4

Beijing Institute of Technology, Beijing, 100811, China

Multimodal Federated Learning Cross-Modal Alignment Multimodal Pretraining Federated Graph Learning Prompt Tuning Graph Foundation Models+1

Jul 15, 2026cs.CV

FM $^2$ : Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging

Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or train from scratch within a single modality, lacking the flexibility to unify tasks. We identify an under-explored challenge, Imaging Modality Heterogeneity, where clients operate under two structural regimes: Overlapped (shared modalities with heterogeneous label distributions) and Non-overlapped (fully disjoint modalities per client). We propose FM

^2

, a unified framework that trains the core backbone from scratch to preserve medical domain fidelity while optionally incorporating biomedical pretrained encoders for vision-language alignment. FM

^2

equips each client with dual Mixture-of-Experts modules (a Class-wise MoE for personalized category knowledge and a Domain-wise MoE for shared cross-modality representations), coupled with a Heterogeneous Modality Alignment (HMA) regularizer that explicitly aligns modality-specific expert parameters, admitting provable

O(1/\sqrt{T})

convergence and generalization guarantees. FM

^2

further incorporates Caption-Enhanced Learning (CEL), where locally retained GPT-4o-generated captions serve as a textual semantic bridge enabling representation transfer across clients with disjoint modalities, and demonstrates extensibility to Federated Medical VQA. Experiments on our MIMH benchmark (classification and CEL) and real-world medical VQA datasets confirm consistent superiority over state-of-the-art federated baselines and strong out-of-modality generalization across all three tasks.

Shengchao Chen, Ting Shu

School of Artificial Intelligence, Shenzhen University Shenzhen, China · Australian AI Institute, University of Technology Sydney Sydney, Australia

Multimodal Federated Learning Cross-Modal Alignment Medical VQA Medical Image Classification Multimodal Foundation Models Federated Learning+1

Jul 13, 2026cs.LG

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations. Addressing this challenge is especially critical for autonomous networked AI operating in safety-sensitive domains, such as content moderation, where reliable retention of prior knowledge underpins system integrity. To overcome this, we propose Federated Continual Multimodal Learning (FedCMM), a framework that embeds continual-learning safeguards into the federated optimization loop at three complementary levels. At the parameter level, modality-aware elastic weight consolidation computes separate Fisher information matrices for the vision encoder, language backbone, and cross-modal projector, providing granular, asymmetry-aware protection against modality-specific forgetting. At the data level, each client trains a lightweight local generative replay module to synthesize raw-data-free embedding-level multimodal replay tuples without any raw data sharing. At the aggregation level, Task-similarity-aware gradient aggregation autonomously filters and reweights client updates by gradient cosine similarity, suppressing conflicting directions and stabilizing the global learning trajectory. Extensive experiments on two benchmarks demonstrate that FedCMM consistently outperforms recent baselines on accuracy and backward transfer, confirming that holistic, modality-aware optimization enables robust evolutive adaptation across heterogeneous networked AI deployments.

Jing Liu, Chenxuanyin Zou, Jiayang Ren +5

Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · College of Future Information Technology, Fudan University, Shanghai 200433, China · Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada +3

Multimodal Federated Learning Generative Replay Multimodal Large Language Models Federated Continual Learning Elastic Weight Consolidation Replay-Based Continual Learning+1

Jul 13, 2026cs.LG

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network environments, Multimodal Large Language Models (MLLMs) serve as cognitive engines for edge devices, yet federated fine-tuning faces substantial challenges in balancing global knowledge aggregation with local adaptation under heterogeneous network conditions. Conventional federated protocols typically rely on uniform parameter aggregation, which conflates domain-invariant features with client-specific nuances, thereby resulting in suboptimal personalization and excessive communication overhead. To address these challenges, we propose PFAdapter, a communication-efficient framework introducing hierarchical LoRA decomposition to explicitly separate adapter parameters into global-shared and local-private components. Query and key projections are assigned to global synchronization for capturing universal multimodal semantics across the network, while value and output projections remain localized for edge-specific adaptation. Additionally, orthogonality regularization based on the Frobenius norm enforces strict separation between these components, preventing redundant feature learning. Selective aggregation protocols synchronize only global-shared components across the federated network, preserving local expertise and reducing communication costs by nearly 50%. Extensive experiments on VQA-RAD, SLAKE, Hateful Memes, and CrisisMMD datasets demonstrate that PFAdapter consistently outperforms state-of-the-art baselines, achieving accuracy improvements ranging from 2.4% to 4.8% across diverse edge intelligence tasks. Consequently, our framework establishes an efficient solution for agentic AI deployment in resource-constrained communication networks.

Jing Liu, Kun Yang, Yan Wang +5

College of Future Information Technology, Fudan University, Shanghai 200433, China · Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou 215316, China · Department of Electrical and Computer Engineering, The University of British Columbia, BC V6T 1Z4, Canada +8

Multimodal Federated Learning Federated Learning Aggregation VLM Adaptation Communication-Efficient Distributed Training Federated Fine-Tuning Federated Learning+2

Jul 7, 2026cs.LG

Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices

Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and computational capacity. Existing update compression schemes typically apply uniform policies across layers and devices, without accounting for modality-specific differences in spectral structure and compressibility. We propose MESH-FL, an entropy-guided matrix product state (MPS) update-compression framework for modality-heterogeneous FL on resource-constrained devices. MESH-FL estimates the spectral entropy of each layer-wise update via truncated singular value decomposition and allocates MPS compression ranks adaptively across layers, modalities, and devices under per-client payload budgets. We show that higher spectral entropy necessitates a higher reconstruction rank under the majorization order on singular-value energy distributions. Building on this result, we prove that the proposed entropy-guided allocation solves a convex surrogate rank-allocation problem, preserves monotonicity under the exact payload model, and achieves convergence with an explicit compression-dependent error term. Experiments on a 15-node heterogeneous Raspberry Pi~4/5 cluster with modality-heterogeneous clients show that MESH-FL achieves up to

56.8\times

compression while surpassing the uncompressed FedAvg baseline in final accuracy by up to 2.01%, and reduces total transmitted data to reach convergence by up to

66\times

.

Quoc Bao Phan, Tuy Tan Nguyen

Department of Electrical and Computer Engineering, Center for Advanced Power Systems, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA

Tensor Networks Multimodal Federated Learning Communication-Efficient Distributed Training Edge Computing Federated Learning Heterogeneous Federated Learning

Jul 7, 2026cs.CV

ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities

In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthesis that is based solely on the available modality. To address these limitations, we propose ProMoE-FL, a Prototype-conditioned Mixture-of-Experts framework for robust missing-modality feature synthesis in multimodal federated learning. ProMoE-FL builds a global client-aware prototype bank that captures clinically meaningful modality priors across institutions. Our Mixture of Experts is conditioned on these prototypes and modality indices to enable direction-aware expert routing for dynamically synthesizing missing features. We perform extensive quantitative and qualitative evaluations on four public chest X-ray datasets (MIMIC-CXR, NIH Open-I, PadChest, and CheXpert) and demonstrate that ProMoE-FL consistently outperforms state-of-the-art methods in both homogeneous as well as the more challenging heterogeneous settings.

Aavash Chhetri, Bibek Niroula, Eduard Vazquez +4

NepAl Applied Mathematics and Informatics Institute for research, Nepal · Fogsphere (Redev.AI Ltd), UK · University of Lausanne, Switzerland +4

Multimodal Federated Learning Missing-Modality Learning Medical Imaging Prototype Learning Mixture-of-Experts Models Federated Learning

Jul 2, 2026cs.LG

QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition

Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems generate heterogeneous and non-independent and identically distributed (non-IID) multimodal sensor streams that degrade conventional FL algorithms, while classical fusion modules introduce substantial parameter overhead and communication cost. This paper proposes QFedAgent, a hybrid quantum-classical personalized FL framework for multi-agent activity recognition. The approach integrates a variational quantum circuit fusion module that models accelerometer--gyroscope interactions through quantum state encoding and entanglement, requiring only 72 quantum rotation parameters versus 33K in classical multi-layer perceptron-based fusion, achieving approximately 10x total parameter reduction. Experiments on the OPPORTUNITY dataset under subject-based non-IID partitions demonstrate 97.7% mean test accuracy, confirming that parameter-efficient quantum fusion remains competitive with conventional federated baselines.

Quoc Bao Phan, Tuy Tan Nguyen

Department of Electrical and Computer Engineering FAMU-FSU College of Engineering, Florida State University Tallahassee, FL 32310, USA

Multimodal Federated Learning Human Activity Recognition Hybrid Quantum-Classical ML Non-IID Federated Learning Federated Learning Multimodal Sensor Fusion

Jun 30, 2026cs.LG

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks. In practice, however, such multimodal graphs are often distributed across decentralized clients, where raw contents and local structures cannot be centrally shared due to privacy constraints. This motivates federated multimodal graph foundation learning, which requires not only transferable representation learning but also intrinsic semantic traceability under strict data isolation. Existing methods usually exchange or store knowledge through parameters, prototypes, embeddings, or compact codebooks, which support optimization and transfer but do not explicitly expose how modality evidence, node semantics, and topology context jointly support predictions. To bridge this gap, we propose FedLAB, a traceable semantic codebook framework that organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context. FedLAB further refines these trace units through federated semantic barycenter pre-training while keeping raw multimodal contents and graph structures local. Extensive experiments on 10 benchmarks and 6 downstream tasks show that FedLAB improves over state-of-the-art baselines by up to 7.53%, while preserving a native semantic trace interface.

Zekai Chen, Kairui Yang, Xuaner Chen +4

Beijing Institute of Technology, Beijing, China

Multimodal Federated Learning Representation Learning Federated Graph Learning Graph Foundation Models Multimodal Foundation Models Multimodal Model Interpretability

Jun 18, 2026cs.LG

Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance. Client-level imbalance occurs when certain clients lack entire modalities, while node-level imbalance occurs when individual nodes exhibit missing visual or textual attributes. While several relevant studies exist, our investigation reveals that they predominantly target graph-agnostic or centralized scenarios, rendering them difficult to adapt directly. To address these challenges, we formalize modality-imbalanced MM-FGL as an implicit graph-aware latent semantic representation synthesis problem. This paradigm recovers missing modal semantics directly within the representation space, thereby maximizing alignment with the original data's semantic distribution and mitigating the high variance induced by missing modalities. To this end, we propose FedMGS (Federated Modality-aware Graph Synthesis), which integrates three core components. The availability-aware graph encoder prevents missing modalities from contaminating local structural propagation. The prototype-guided latent semantic synthesizer establishes cross-client semantic anchors for unavailable modalities. The reliability-calibrated semantic fusion mechanism regulates the impact of recovered latent representations prior to predictive readout. Extensive experiments on four tasks show that FedMGS consistently outperforms competitive baselines with gains up to 17.41% with best efficiency-performance tradeoff.

Zhengyu Wu, Hongchao Qin, Xunkai Li +3

Multimodal Federated Learning Missing-Modality Learning Federated Graph Learning Multimodal Graph Learning Multimodal Fusion

Jun 8, 2026cs.LG

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency. Unlike random instance-wise missingness, a deficient client lacks the local semantic basis needed to reconstruct the absent modality. More importantly, in graph learning, incomplete representations initialize message passing, so imputation errors can be filtered, mixed, and amplified by the receiving topology. To address this gap, we propose \textbf{PRISM} (\textbf{P}roactive \textbf{R}etrieval and \textbf{I}mputation via \textbf{S}tructural \textbf{M}eta-prompting), a topology-aware federated cross-modal imputation framework. Rather than reconstructing the missing modality solely from local observations, PRISM recovers missing-modality semantics from the federation and introduces them into local graph propagation under topology-aware control. Experiments on six multimodal graph datasets across graph-centric and modality-centric tasks show that PRISM consistently improves modality-deficient clients, outperforming state-of-the-art baselines by \textbf{4.48}% on average.

Zekai Chen, Miao Zhang, Jiayang Xing +4

Beijing Institute of Technology, Beijing, 100081, China

Multimodal Federated Learning Missing-Modality Learning Federated Graph Learning Multimodal Graph Learning

Jun 5, 2026cs.LG

Federated Foundation Models over Vehicular Networks

This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL). Given the largely underexplored nature of this research direction, we first introduce the fundamental training/fine-tuning principles of M3T FedFMs. We then discuss a range of their representative use cases in vehicular networks, illustrating the significant potential of M3T FedFMs to enable next-generation vehicular intelligence. Afterwards, we identify key constraints inherent to vehicular environments that challenge the practical deployment of M3T FedFMs, and articulate a set of forward-looking research directions to address these challenges. Furthermore, through a case study conducted on a real-world vehicular dataset (i.e., Waymo Open Dataset), we demonstrate the promise of M3T FedFMs for vehicular networks and release our implementation to facilitate reproducibility and stimulate research in this emerging area (repository: https://github.com/KasraBorazjani/vehicular-fedfm)

Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali +5

Multimodal Federated Learning Intelligent Transportation Systems Multimodal Learning Federated Learning

Jun 1, 2026cs.DC

Boosting Multimodal Federated Learning via Chained Modality Optimization

Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modality availability. However, most existing MMFL methods cast multimodal training as a joint optimization problem, overlooking a key bottleneck: modality competition, where dominant modalities suppress weaker ones and lead to suboptimal global models. To address this, we propose FedMChain, a balanced MMFL framework that structures federated multimodal training as a chain of modality-wise phases. This phase-wise design gives each modality a dedicated local optimization window on multimodal clients to mitigate modality competition, and further promotes cross-modal complementarity via an error-compensated regularizer. On the server side, we employ a sparse sign-guided aggregation strategy that leverages directional sign agreement for robust intra-modality aggregation, avoids destructive averaging, and supports less frequent synchronization to reduce communication overhead. Extensive experiments on multimodal benchmarks demonstrate that FedMChain consistently improves predictive performance while requiring less frequent communication than baselines.

Zixin Zhang, Fan Qi, Shuai Li +2

College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, China · School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China

Multimodal Federated Learning Federated Learning Aggregation Federated Learning

May 12, 2026cs.LG

Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity

Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applications. However, real-world graphs are often isolated due to data-sharing limitations across multiple parties, and their modalities are frequently incomplete. This highlights an urgent need to develop a robust federated approach. However, we find that existing methods remain insufficient. On the one hand, centralized MGL methods that handle missing modalities overlook the knowledge sharing and generalization in federated scenarios. On the other hand, while federated MGL methods have become increasingly mature, they primarily target non-graph data. Based on these technologies, we identify a two-stage pipeline wherein client-side completion reconstructs missing modalities, and server-side aggregation integrates the client-updated parameters of both the modality generator and the backbone models. Although this serves as a general solution, we identify two primary challenges in achieving greater robustness: (1) Topology-Isolated Local Completion: Client-side modality generation struggles to effectively leverage global semantics. (2) Reliability-Imbalanced Global Aggregation: Server-side multi-party collaboration is hindered by client updates with varying modality availability and recovery reliability. To address these challenges, we propose \textsc{FedMPO}, which utilizes topology-aware cross-modal generation to recover missing features using comprehensive graph context, missing-aware expert routing to locally filter out noisy recovered signals, and reliability-aware aggregation to appropriately down-weight unreliable updates. Extensive experiments on 3 tasks across 6 datasets demonstrate that FedMPO outperforms baselines, achieving performance gains of up to 4.10% and 5.65% in high-missing and non-IID settings.

Sirui Zhang, Haonan Wang, Xunkai Li +5

Beijing Institute of Technology

Multimodal Federated Learning Missing-Modality Learning Multimodal Robustness Federated Graph Learning Multimodal Graph Learning Heterogeneous Federated Learning

May 12, 2026cs.LG

STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning

Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. As graph data increasingly contain multimodal node attributes such as text and images, multimodal federated graph learning (MM-FGL) has become an important yet substantially harder setting. The key challenge is that clients from different modality domains may not share a common semantic space: even for the same concept, their local encoders can produce inconsistent representations before collaboration begins. This makes direct parameter coordination unreliable and further causes two downstream problems: forcing heterogeneous client representations into a naively shared semantic space may create false semantic agreement, and graph message passing may amplify residual inconsistency across neighborhoods. To address this issue, we propose \textbf{STAGE}, a protocol-first framework for MM-FGL. Instead of relying on direct parameter averaging, STAGE builds a shared semantic space that first translates heterogeneous multimodal features into comparable representations and then regulates how these representations propagate over local graph structures. In this way, STAGE not only improves cross-client semantic calibration, but also reduces the risk of inconsistency amplification during graph learning. Extensive experiments on 8 multimodal-attributed graphs across 5 graph-centric and modality-centric tasks show that STAGE consistently achieves state-of-the-art performance while reducing per-round communication payload.

Zekai Chen, Xun Wu, Xunkai Li +3

Beijing Institute of Technology, Beijing, 100081, China · Minzu University of China, Beijing, 100081, China

Multimodal Federated Learning Cross-Modal Representation Learning Federated Graph Learning Multimodal Graph Learning Federated Learning

May 10, 2026cs.LG

M $^2$ FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices

Accurate air quality prediction is essential for public health, environmental monitoring, and industrial safety. However, most existing approaches rely on centralized learning paradigms, which introduce challenges related to scalability, privacy preservation, and communication overhead in distributed Internet of Things (IoT) environments. Moreover, current federated learning (FL) based solutions predominantly utilize unimodal data, limiting their capability to capture complex environmental patterns. To address these limitations, we propose M

^2

FedAQI, a lightweight multimodal federated framework for decentralized Air Quality Index (AQI) prediction across heterogeneous edge devices. The proposed framework integrates visual and tabular modalities through a feature modulation based fusion mechanism that enables efficient cross-modal interaction while maintaining low computational overhead. M

^2

FedAQI is evaluated on two benchmark datasets, PM25Vision and TRAQID, for both classification and regression tasks under centralized and federated settings. Experimental results demonstrate that M

^2

FedAQI consistently outperforms existing approaches, achieving improvements of up to 11.0% in Accuracy, 3.53% in AUC, 12.2% in F1-score, and 18.0% in

R^2

, while reducing MAE and RMSE by up to 25.4% and 20.4%, respectively, compared with the strongest baselines. Furthermore, deployment on heterogeneous edge devices demonstrates efficient resource utilization in terms of communication overhead, memory footprint, and computational cost. To enhance communication security, TLS-based authentication is incorporated to ensure secure client participation and protect the FL communication channel from unauthorized third-party access without modifying the underlying FL protocol.

Manjil Nepal, Kimsie Phan, Tamoghna Ojha +2

Department of Computer Science and Engineering, SRM University-AP, India · Department of Mathematics and Computing, IIT (ISM) Dhanbad, India · Department of Mathematics and the Department of Computer Science, University of Central Florida, USA

Multimodal Federated Learning Air Quality Prediction Edge ML Multimodal Fusion Federated Learning Heterogeneous Federated Learning

May 8, 2026cs.LG

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment

Device-free localization trains models from heterogeneous wireless and visual sensors (e.g., Wi-Fi, LiDAR) distributed across edge devices. Federated learning offers a privacy-respecting framework, but is brittle when clients differ in sensor modality and resolution, when their data distributions drift, and when privacy noise destroys the structural signal needed for localization. We propose UMEDA, a graph federated learning framework in which clients form nodes of a global graph that share a continuous integral operator, and aggregation is reformulated as spectral signal processing on this operator. Each client encodes its local sensors with a linear-attention layer whose kernel spectrum is low-rank filtered, suppressing modality-specific residuals so clients with different sensors align in a common low-rank subspace. The server then aggregates client updates via a diffusion model over the kernel's spectral coefficients, treating updates as discretizations of a shared operator rather than topology-bound weights -- this absorbs varying graph sizes and missing modalities without node-wise correspondence. To balance privacy and utility, we add an anisotropic differential-privacy mechanism that projects noise preferentially into the null space of the signal subspace, preserving dominant eigendirections while ensuring formal

(ε, δ)

-DP under gradient clipping. On MM-Fi and the RELI11D out-of-distribution benchmark, UMEDA outperforms state-of-the-art federated baselines in accuracy, convergence, and communication efficiency, particularly under high modality heterogeneity and tight privacy budgets.

Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2

National Taiwan University · RIKEN-CCS · The University of Osaka +1

Multimodal Federated Learning Federated Learning Aggregation Communication-Efficient Distributed Training Federated Graph Learning Differentially Private Federated Learning Federated Learning+2

May 1, 2026cs.NI

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles forgotten knowledge across both modalities and client gradient subspaces, hindering federated unlearning. Previous federated unlearning approaches neither sever the cross-modal reconstruction channel mediated by bilinear coupling nor separate forget-exclusive update directions from those shared with retained clients. We identify an Anchor Principle for federated multimodal contrastive unlearning: forgotten alignments persist through three residual anchors arising from bilinear cross-modal coupling, principal-angle subspace entanglement, and continued federated updates. At the modality level, we show that bilateral displacement of both visual and language branches closes the cross-modal reconstruction channel. Correspondingly, our method addresses subspace entanglement through Cosine--Sine decomposition of client-update subspaces, isolating forget-exclusive directions from retain support. Moreover, we propose a direction-selective Forget Lock that bounds residual drift across rounds. Combining these strategies, we present EASE, an Entanglement-Aware Subspace Excision framework that closes all three anchor channels under a unified design. EASE demonstrates consistent superiority across multiple datasets and unlearning scenarios, for instance, matching the retrain reference to within 0.2 and 4.2 R@1 points on the forget and retain sides under client unlearning on Flickr30K with CLIP-B/32.

Zihao Ding, Beining Wu, Jun Huang

Department of Electrical Engineering and Computer Science South Dakota State University Brookings, SD 57007, USA

Cross-Modal Learning Multimodal Federated Learning Federated Unlearning Cross-Modal Alignment Multimodal Contrastive Learning Machine Unlearning+1

Apr 25, 2026cs.LG

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning

Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-modality missingness caused by sensor intermittency or irregular sampling. Existing methods implicitly represent unobserved data via architectural alignment or missing embeddings, often failing to recover the true distribution and yielding sub-optimal performance. We propose CondI, a federated framework explicitly addressing this missingness using conditional diffusion models. CondI employs a two-phase training pipeline: first, imputing unobserved temporal components using available multimodal context and conditional embeddings; second, optimizing modality-specific extractors and joint embedding spaces. During inference, imputed raw data pass through trained extractors to generate robust features, providing a holistic representation for downstream tasks. Explicit data imputation ensures models operate on complete semantic structures, significantly enhancing resilience against severe data incompleteness. Experiments on three clinical datasets (PTB-XL, SLEEP-EDF, MIMIC-IV) demonstrate CondI achieves comparable results to state-of-the-art baselines. Code: https://github.com/ZhengWugeng/CondI

Wugeng Zheng, Ziwen Kan, Katie Wang +2

University of Central Florida · Independent Researcher

Multimodal Federated Learning Time Series Imputation Incomplete Data Imputation Learning with Missing Data Federated Learning