Federated Learning

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91 papers in the last four weeks, up 176% on the four weeks before. 0.9% of all new papers.

Jul 13Week of Sep 28

Latest papers 595

Sep 30, 2026cs.LG

Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Therefore, it is important to have a mechanism that ensures fast convergence of the global model as well as good FL participation rate. Another issue for the convergence of a model in FL is the non-independent and identically distributed (non-iid) data across the clients. Prior approaches based on probabilistic client selection do not work well under non-iid data especially when the number of clients is small. We show scenarios where such approaches fail and propose a joint client-training data selection algorithm for fast convergence of FL models. Our experiments on CIFAR-100 dataset show that convergence of the FL model can be significantly improved over prior works that can consider non-iid data and heterogeneous computation and higher model accuracy.

Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park

Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan

Client Selection in Federated Learning Non-IID Federated Learning Training Data Selection Federated Learning Heterogeneous Federated Learning

Sep 30, 2026cs.LG

HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices

Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial memory, whereas zeroth-order (ZO) optimization suffers from severe convergence slowdown. To resolve this dilemma, we introduce HO-FL, a hybrid-order FL framework that trains a model's bottom segment with ZO optimization and its top segment with FO optimization. Each device can flexibly select its order boundary according to its memory budget while participating in the training of the same global model. Moreover, our convergence analysis reveals a new, fundamental trade-off: clients with larger FO-trained segments can provide more accurate updates, but favoring them can underrepresent other clients' data. We connect this trade-off to the bias and variance of actual multi-step local updates, yielding a sampling optimization problem and a practical dimension-aware approximation with direct model averaging. Experiments on language tasks examine task performance, client memory, and sampling under data heterogeneity. The results show that hybrid-order local training can retain much of the full-FO performance with substantially lower client memory requirements. Our code is available at https://github.com/HKU-WILL-Lab/HO-FL.

Qiyuan Chen, Xian Wu, Yanan Ma +1

University of Hong Kong · City University of Hong Kong

On-Device Training Federated Learning Heterogeneous Federated Learning

Sep 29, 2026cs.CR

Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning

Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without exchanging patient records. This privacy promise, however, is increasingly contested: a malicious or honest-but-curious server can launch model inversion attacks (MIAs) that reconstruct private patient images directly from shared model updates, and recent scalable, closed-form attacks penetrate even secure aggregation at clinically realistic batch sizes. Existing defenses face an unsatisfactory dilemma. Gradient-perturbation methods such as differential privacy and pruning trade away the diagnostic accuracy on which clinical reliability depends, while cryptographic protocols add system complexity yet still leave updates exposed to these scalable attacks. We propose Aegis, a principled client-side defense that breaks this dilemma without perturbing patient data or modifying the FL protocol. Our key insight is that the success of every known MIA is fundamentally bounded by the local batch size relative to the model's leakage capacity; once this limit is exceeded, distinct samples collide and reconstructions collapse into indistinguishable mixtures. Aegis turns this universal bottleneck into a defense: each client superimposes onto its real update a masking gradient computed on locally synthesized, task-relevant data, deliberately pushing the effective batch beyond the attack's recovery capacity. We complement the design with theoretical convergence guarantees under standard convex assumptions and evaluate Aegis on MNIST, CIFAR-10, and three MedMNIST modalities (chest X-ray, abdominal CT, colon pathology). Aegis neutralizes three state-of-the-art MIAs while preserving model utility and incurring only modest overhead, offering a practical privacy primitive for medical FL.

Chaoyu Zhang, Shanghao Shi, Heng Jin +3

Virginia Tech · Washington University in St. Louis · University of South Florida

Model Inversion Attacks Federated Learning

Sep 29, 2026cs.CV

FedSocket: Recipient-Executable Knowledge Exchange for Heterogeneous Multimodal Federated Learning

Federated knowledge must remain usable by recipients with different modalities, private architectures, and tasks. We present FedSocket, which makes recipient execution a design requirement of the exchanged model. A shared Q combines recipient-computable inputs, task-owned outputs, and ownership-aware aggregation, connecting heterogeneous private models through a common prediction interface. Private models teach local Q copies; the returned Q supports local learning and Joint inference, with only Q parameters and counts exchanged. Across six datasets, FedSocket improves missing-modality recipient accuracy over Local by 14.44 and 15.51 percentage points on MELD and UCF-51. Under matched inference capacity, Joint exceeds independent ensembles by 11.06 points in UCF-51 accuracy and 4.87 points in mean bidirectional Flickr30k R@1. Joint also improves over Q alone on all four heterogeneous endpoints, demonstrating the value of combining local and exchanged predictions. Teacher controls, sharing-path interventions, and component factorials identify the roles of supervision, sharing, and deployment. FedSocket makes exchanged knowledge directly usable from federated training to recipient inference.

Xinyuan Zhao

Sun Yat-sen University

Cross-Modal Knowledge Distillation Federated Knowledge Distillation Federated Learning

Sep 29, 2026cs.AI

FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning

Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneity makes a single global adapter insufficient for personalized prediction. Existing personalized methods typically use the same low-rank structure for both shared and private adaptation, overlooking their distinct requirements for aggregation and personalization. We propose FedLAFP, a role-aware framework that couples a compact, globally aggregated LoRA branch with a client-private, full-rank-capable RandLoRA branch. The shared branch provides an efficient interface for transferring common knowledge, whereas the private branch combines fixed random low-rank bases with learned scaling coefficients to provide expressive client-specific adaptation without additional communication. Client- and layer-specific mixing coefficients jointly fuse the two branches, and only the shared LoRA parameters are exchanged. A controlled linear study supports this role assignment: LoRA yields more aligned client updates and lower aggregation error, while RandLoRA more accurately recovers client-specific residuals. Experiments across four visual recognition benchmarks show that FedLAFP consistently outperforms local-only and federated LoRA baselines, achieving an average personalized accuracy of

86.93\%

and exceeding the best baseline average by

1.30

percentage points.

Mengjun Yi, Huaian Gu, Yinghao Ai +2

State Key Laboratory for Novel Software Technology · School of Artificial Intelligence · School of Computer Science +1

Low-Rank Adaptation Federated Fine-Tuning Federated Learning Parameter-Efficient Fine-Tuning

Sep 29, 2026cs.CR

OPFL: Optimistic Verification of Federated Learning via Empirical Boundary

Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into local training makes it difficult to verify whether clients follow the prescribed training procedure or submit malicious updates, such as model poisoning. A natural approach is to replay client training for verification. However, privacy-preserving replay produces numerical results that cannot be directly matched with local client execution because the two run in different environments. We present OPFL, an optimistic verification framework for privacy-preserving federated learning. To protect data privacy, OPFL performs replay inside secure multi-party computation (MPC). Although gradients computed on MPC and local GPUs are not bitwise identical, we observe that their absolute differences are stable and bounded. OPFL therefore calibrates an empirical boundary offline and uses it to distinguish benign numerical deviations from malicious manipulation. To reduce the cost of expensive MPC replay, OPFL adopts optimistic verification by post auditing only sampled training steps. Experiments on LeNet, BERT, and Qwen show that the boundary generalizes across datasets, input lengths, and GPUs, while achieving

0

% ASR against model poisoning and PGD-based attacks. On a LeNet workload, at

p=0.01

, OPFL is approximately

98.6\times

faster than full MPC-based FL and

625.5\times

faster than ZK-based approach.

Hongxu Su, Jianzhu Yao, Xuechao Wang +1

Federated Learning

Sep 29, 2026cs.AI

CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning

Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under heterogeneous client data: a structural aggregation mismatch caused by independently averaging LoRA factors, and a statistical collaboration mismatch caused by enforcing a single global adapter across divergent clients. To address these issues, we propose CF-LoRA, a clustered federated LoRA fine-tuning framework that combines decoupled factor aggregation with adaptation-aware client clustering. CF-LoRA first learns a globally shared

A

factor while retaining personalized

B_i

factors, then identifies clients with similar adaptation patterns based on the cosine similarity of their learned

B_i

factors, and finally performs intra-cluster

B

-factor aggregation with a frozen

A

factor. By decoupling LoRA factor aggregation, CF-LoRA preserves the low-rank structure and mitigates the structural aggregation mismatch, while adaptation-aware clustering promotes collaboration among clients with similar adaptation patterns and reduces negative transfer caused by statistical heterogeneity. Experiments on four language tasks and four vision datasets with RoBERTa and ViT show that CF-LoRA achieves the highest average accuracy in both modalities while communicating only one LoRA factor per optimization round.

Mengjun Yi, Langxing Yang, Suhan Guo +2

State Key Laboratory for Novel Software Technology · School of Artificial Intelligence School of Electronic Science and Engineering Nanjing University

Federated Learning Aggregation Federated Fine-Tuning Clustered Federated Learning Federated Learning Parameter-Efficient Fine-Tuning Heterogeneous Federated Learning

Sep 28, 2026cs.CR

DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning

Decentralized Federated Learning (DFL) eliminates the central aggregation server, reducing the single point of observation that traditional defenses against attacks rely on. As a result, peer-to-peer networks become exposed to malicious updates containing backdoors or semantic poisoning, since such updates can remain close to benign ones in the parameter space while behaving very differently. This may evade defenses based on passive parameter inspection. However, existing deception-based defenses have mainly been designed for centralized FL and do not jointly address local observation, poisoning propagation, source attribution, and containment in strictly serverless DFL. To address these limitations, this paper presents DecoyTrace, a proactive cyber deception-based defense for strictly serverless DFL environments. DecoyTrace deploys a mobile DecoyNode that generates decoy challenges using chaotic maps, disseminates a dual model (clean vs. decoy) based on neighbor trust, and evaluates them using three-state semantic metrics. Upon confirmation, a distributed protocol isolates the source and performs a model reset or recovery to preserve training progress. Evaluated across sixty configurations on the NEBULA platform (five datasets, three topologies, and four attack/defense scenarios), DecoyTrace systematically restores lost utility. The F1-score remains within 0.03 of the baseline on MNIST/FashionMNIST (mitigating drops of up to 0.37), matches or exceeds the baseline on EMNIST and CIFAR-100, and remains between 0.05 and 0.10 below the baseline on CIFAR-10, the most visually complex convolutional scenario evaluated. Furthermore, containment reduces CPU and network usage by up to two-thirds. These results demonstrate the feasibility of unifying deception, identification, and containment in DFL, while also identifying its limitations in complex tasks and multi-attractor threat models.

Pedro Beltrán-López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli +2

Department of Information and Communication Engineering, University of Murcia, Murcia, 30100, Spain

Byzantine-Robust Federated Learning Backdoor Attacks in Federated Learning Cybersecurity Federated Learning

Sep 28, 2026cs.LG

Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs

Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This assumption limits their applicability to practical vehicular environments, where vehicles may perform heterogeneous but related perception tasks with different output spaces. This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders. EN-HMTFL enables vehicles performing different tasks to collaboratively learn a transferable feature representation while preserving their task-specific models locally. Only the encoder is exchanged and aggregated through the hierarchy, whereas raw data and local decoder parameters remain at the vehicles. The proposed framework is evaluated on the MNIST and GTSRB datasets in different vehicular scenarios. Across the evaluated scenarios, EN-HMTFL improves accuracy by up to 24.0% relative to the compared representation-sharing benchmark. In scenarios where EN-HMTFL converges earlier, the reduction reaches up to 69 communication rounds (28.8%).

M. Saeid HaghighiFard, Sinem Coleri

Department of Electrical and Electronics Engineering, Koc¸ University, Istanbul, Türkiye

Hierarchical Federated Learning Intelligent Transportation Systems Federated Learning Heterogeneous Federated Learning

Sep 28, 2026cs.LG

Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study

Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept stage. Here we present FLIP (Federated Learning Interoperability Platform), an open-source, multi-application platform that makes FL training and evaluation repeatable. FLIP implements common FL workflows as a set of composable services: cohort queries against per-site structured databases, on-demand DICOM retrieval from institutional PACS, per-site project approval, and reusable FL job types. To demonstrate FLIP, we ran two distinct use cases, federated fine-tuning and federated evaluation, on synthetic chest X-ray cohorts across two client nodes based in the United Kingdom (UK) and Thailand. In FLIP, each institution independently approves its participation in each project and operates its own node under local IT security processes. This study makes an operational rather than an algorithmic claim. It does not compare federated with centralised training; for that question, we refer the reader to existing systematic reviews and meta-analyses. The central result is evidence that such platforms enable international FL collaboration and improve repeatability, auditability, and site-specific governance. We also present a comprehensive comparison of existing platforms to help researchers and operators choose the right platform for their use case.

Rafael Garcia-Dias, Alexandre Triay Bagur, Chayanin Tangwiriyasakul +20

School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK · Bangkok Dusit Medical Services, Bangkok, Thailand

Healthcare ML Reproducibility Federated Learning

Sep 28, 2026cs.LG

CTP-FL: Common-Trajectory Gradient Prediction for Federated Learning

Communication-efficient federated optimization commonly spends several gradient evaluations between server updates. Existing local-update methods use this computation to advance an independent model on each client. Under heterogeneous data, however, these models evaluate gradients at different locations, making the aggregated update difficult to interpret as a gradient of the global objective. We study an alternative use of the same computation budget: \emph{evaluate the global objective along a shared, predicted path}. We propose Common-Trajectory Predictive Federated Learning (\texttt{CTP-FL}). At each round, all clients construct the same sequence of query points from the current global model and the previous aggregated direction, evaluate

K

stochastic gradients along this sequence, and upload their average. The server then performs a single global update. Thus, \texttt{CTP-FL} uses

K

mini-batch gradients per client and one model-sized vector in each communication direction, matching the per-round computation and communication of full-participation FedAvg-M. Shared query points make the aggregated direction an unbiased estimator of the average \emph{global} gradient along the predicted path. The remaining discrepancy from the gradient at the current model is controlled by the path length, without assuming bounded client-gradient dissimilarity or bounded gradients. For smooth non-convex objectives, we establish an

\mathcal{O}\!\left( \sqrt{LΔσ^2/(NKR)}+LΔ/R \right)

average-stationarity bound under full participation. The analysis isolates a testable trade-off: extending the prediction path provides more forward-looking gradient information but increases its displacement bias.

Junkang Liu

Tianjin University

Federated Learning

Sep 28, 2026cs.RO

RoboFL: Federated Expert Assembly for World Action Models

Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heterogeneous. A natural federated solution is to let each client adapt a shared foundation model through parameter-efficient fine-tuning, avoiding the exchange of full-model updates. However, federating these adapters is nontrivial, as naive aggregation can entangle incompatible updates, while incorporating MoE-style routing into federated aggregation may dilute specialization and destabilize expert selection. We present RoboFL, which instantiates MoSAIC (Mixture of Slotted Adapters) for federated world-action learning. MoSAIC directly installs locally trained LoRA adapters as the expert branches of a server MoE. Server-side routers learn token assignments over these prior-informed branches while jointly refining routing and expert parameters. Foresight-to-Action Routing Distillation (FARD) aligns routing across the model's three paths, while Path-Consensus Expert Aggregation (PCEA) converts complete expert updates into a compact global adapter for personalized redistribution. Experiments on RoboTwin 2.0, RLBench, and a real-world Franka robot arm show the superiority of RoboFL with structured expert assembly, as it outperforms centralized PEFT InternVLA-A1 by 12.23% on the Franka arm, while reducing per-round client communication by up to 86.81% relative to MoE-based federated VLA baselines.

Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8

Nanjing University · Hong Kong University of Science and Technology · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University

Mixture of Experts World Action Models Federated Fine-Tuning Federated Learning Parameter-Efficient Fine-Tuning

Sep 28, 2026cs.CR

Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure

Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetry remain indistinguishable from honest contributions. We repurpose cyber-physical process invariants, such as conservation laws and actuator couplings, from runtime detection heuristics into a verifiable admission requirement for federated updates, mined automatically from clean operational data. We evaluate this admission gate across two physical water testbeds (SWaT, WADI) and a distribution benchmark (BATADAL), testing seven aggregation rules against telemetry fabrication, exposure-only replay poisoning, and an invariant-aware adaptive adversary. Across three testbeds the mined invariants reject none of 100 honest shards and all naively fabricated ones, including optimised perturbations that FoolsGold admits in full. On real telemetry, five mined invariants detect 12 of SWaT's 35 attacks, while nine invariants detect 20, with no honest shard rejected. With nine rules, the physics gate recovers 69--100% of the targeted-attack recall lost to replay poisoning, and 54--100% of that lost to fabricated telemetry, across five standard aggregators. To reconcile physical admission control with federated data privacy, we show invariant compliance using zero-knowledge proofs (zk-SNARKs) to allow clients to prove batch adherence without revealing operational telemetry.

Jeff Nijsse, Shu Su, Benjamin Oholeguy +1

Department of Software Engineering, RMIT University, Hanoi, Vietnam · Department of Mathematical Sciences, Auckland University of Technology, New Zealand · Independent Researcher, New Zealand +1

Byzantine-Robust Federated Learning Industrial Anomaly Detection Federated Learning

Sep 28, 2026cs.MA

Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and edge intelligence. However, conventional FL approaches rely on static client participation and fixed aggregation strategies, which limits their effectiveness under non-IID data distributions, heterogeneous client behavior, and noisy or unreliable updates. To overcome these issuess, this paper proposes an Agentic Federated Learning (AFL) framework that integrates lightweight rule-based autonomous agents at both client and server levels. The proposed framework introduces a Client-Side Agent (CSA) that dynamically adapts local training parameters, controls participa- tion, and evaluates update reliability, while a Server-Side Orchestrator Agent (SSOA) performs quality-aware client selection and adaptive aggregation. Unlike traditional FL methods, AFL enables context-aware decision-making during the training process, improving adaptability and robustness in dynamic distributed environments. Extensive experiments conducted on the CIFAR-10 dataset under IID, non-IID, and noisy-client settings demonstrate that AFL consistently outperforms standard base- lines including FedAvg and FedProx. Experimental results show improvements in classification accuracy, convergence speed, robustness against corrupted updates, and communication efficiency. Ablation studies further confirm the complementary contributions of CSA and SSOA, while statistical analysis validates the significance of the observed gains. The proposed AFL framework demonstrates that incorporating autonomous agentic reasoning into federated learning provides an effective and practical solution for intelligent, adaptive, and robust distributed learning systems.

Deepthy K. Bhaskar, VP Binu, B Minimol

Department of Computer Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India. · Department of Biomedical Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India.

Federated Learning Aggregation Client Selection in Federated Learning Non-IID Federated Learning Federated Learning

Sep 24, 2026cs.LG

SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM

Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal--dual method that allows the number of local function-value updates between neighbor exchanges to grow with the computation budget while preserving the nonprivate convergence order. For smooth nonconvex objectives under uniform query-moment bounds, the prescribed nonprivate schedule achieves a time-averaged stationarity and consensus bound of

\mathcal{O}(T^{-1/3})

using only

Θ(T^{2/3})

communication rounds, where

T

is the number of local updates per client. For private training, the accumulated data-dependent increment is isolated from the graph correction, allowing one protected state per client and round to generate all outgoing messages. We prove client-level differential privacy for the full interactive transcript and quantify the resulting optimization error over a finite horizon. Experiments on four classification tasks show that SPADE-DFL achieves higher mean test accuracy than existing decentralized learning methods.

Mengli Wei, Mengkai Zhu, Jiawen Chen +2

School of Mathematics, Southeast University, Nanjing 210096, China

Communication-Efficient Distributed Training Black-Box Optimization Alternating Direction Method of Multipliers Decentralized Learning Differentially Private Federated Learning Derivative-Free Optimization+1

Sep 24, 2026stat.ML

Personalised federated learning for Riemannian and Euclidean EEG decoding

Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them. We consider two light EEG decoders, the Riemannian SPDNet and the Euclidean EEGNet. Both split into a trunk, which builds a latent representation, and a head, which classifies it. Inter-subject variability, however, makes a single shared FL model a poor fit for each subject. Personalised FL addresses this: all subjects learn a common trunk, and each subject keeps its own head. We adapt it for SPDNet and study its effects against standard FL and centralised training, with EEGNet as a Euclidean baseline. Experiments cover three motor-imagery datasets that span diverse regimes in channels, subjects and classes. We observe that personalised SPDNet reaches higher accuracy than both standard FL and centralised training, while converging in fewer rounds and communicating fewer parameters than standard FL. It also outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.

Thibault Pautrel, Florent Bouchard, Ammar Mian +1

Universit´e Paris-Saclay, CNRS, CentraleSup´elec, L2S, France · Universit´e Savoie Mont-Blanc, LISTIC, France

EEG Decoding Federated Learning

Sep 23, 2026cs.LG

Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities

While Federated Learning (FL) has been widely adopted for protecting user privacy in machine learning, it remains vulnerable to various robustness challenges, including performance-impairment risks, information-stealing threats, and aggregation vulnerabilities. This work offers a holistic synthesis of FL robustness along three tightly coupled angles: (i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces, (ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies, and (iii) a layered taxonomy of defensive strategies. We rigorously examine current evaluation practices for FL robustness and identify major applications and open research challenges to guide future research.

Pravija Raj P, Ashish Gupta, Andrea Augello +1

Department of Computer Science and Engineering, BITS Pilani Dubai Campus, Dubai, UAE · Department of Engineering, University of Palermo, Palermo, Italy · Department of Computer Science, Missouri University of Science and Technology, Rolla, USA

Federated Learning Aggregation Adversarial Robustness Federated Learning

Sep 23, 2026cs.LG

Federated Learning of AnDE Classifiers

This work presents a federated framework for training Averaged

n

-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses on the discriminative setting, where model weights are learned locally and aggregated globally, supporting any dependency order

n

. This design allows federated training without transmitting semantically meaningful parameters, improving privacy. Additionally, generative AnDE models are federated to provide a comparative baseline, with optional differential privacy applied to the aggregation of probability tables. Experiments on 12 discrete datasets show that discriminative models with

n \geq 1

consistently outperform federated Naive Bayes (NB,

n=0

), and that privacy-preserving aggregation is effective with limited accuracy loss. These results establish federated AnDE as a viable and privacy-preserving framework, showing that probabilistic models remain applicable in modern federated learning settings.

Pablo Torrijos, Juan C. Alfaro, José A. Gámez +1

Departamento de Sistemas Informáticos. Universidad de Castilla-La Mancha, 02071 Albacete, Spain. · Instituto de Investigación en Informática de Albacete. Universidad de Castilla-La Mancha, 02071 Albacete, Spain.

Federated Learning

Sep 23, 2026cs.LG

Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness

Multi-center clinical studies and biomedical research collaborations increasingly seek to utilize data across centers to build models that generalize beyond any single center. This creates two distinct challenges: data protection regulations may restrict the sharing of raw patient data across institutions, while centers may collect only partially overlapping sets of features under different protocols. Federated learning enables collaborative model training without centralizing raw data. However, existing federated imputation methods rarely evaluate feature-level missingness, in which entire features are unobserved at some centers. To address this setting, we adapt the ReMasker masked autoencoder to federated learning (Fed-ReMasker), enabling centers to impute features never observed locally by leveraging knowledge learned across collaborating centers. We evaluate Fed-ReMasker in a benchmark spanning synthetic datasets with linear and nonlinear relationships and real-world tabular datasets, including clinical data. The benchmark varies the number of centers, the missingness ratios, and client heterogeneity. Fed-ReMasker achieves the lowest imputation error in 93.2% of value-level and 96.7% of feature-level scenarios in the homogeneous benchmark. It also remains robust to client heterogeneity using simple federated averaging, outperforming all baselines in all 36 value-level scenarios and each baseline in at least 35 of 36 feature-level scenarios, and comes within 3.0% on average of a centralized model trained on the pooled data.

Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman +1

ARTORG Center for Biomedical Engineering Research University of Bern, Bern, Switzerland · Graduate School for Cellular and Biomedical Sciences University of Bern, Bern, Switzerland

Incomplete Data Imputation Masked Autoencoders Learning with Missing Data Federated Learning

Sep 23, 2026stat.ML

FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints

Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, or rejection of creditworthy applicants. Cross-institutional data-sharing constraints make this problem especially difficult for smaller lenders with limited training data. We introduce FedIncome, a federated learning framework for income estimation that enables institutions to train a shared model without pooling raw borrower records. Using more than one million LendingClub loans partitioned into

50

state-level clients, we simulate a heterogeneous lending consortium. The best federated model achieves out-of-time

R^2=0.608

, compared with

0.619

for a pooled centralised benchmark. Small-sample clients obtain an average out-of-time

R^2

improvement of

3.8

percentage points relative to the pooled centralised benchmark, while the fitted client-level relationship places the empirical crossover at approximately

4,790

training observations in this setting. When pooling is infeasible and the relevant alternative is local-only training, federation improves out-of-time performance across all sample-size groups, with the largest gains for data-scarce clients. We also combine federated income estimates with state- and income-specific debt-to-income thresholds. In a retrospective decision analysis, replacing reported income with the federated estimate increases simulated approval rates with only modest changes in observed default rates. FedIncome supports collaborative learning under data-locality constraints with little aggregate loss relative to pooled training and larger gains relative to local-only estimation.

Sultan Amed, Tanmay Sen, Sayantan Banerjee

OM & QT Area, Indian Institute of Management Indore, M.P. 453556, India. · SQC & OR Unit, Indian Statistical Institute Kolkata, W.B. 700108, India

Credit Scoring Finance Federated Learning Heterogeneous Federated Learning

Sep 23, 2026cs.CV

A generalizable structural brain MRI foundation model built through dual-priority federated pretraining

Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite privacy and governance constraints. Such pooling optimization can overemphasize cohort size and overlook complementary information from smaller, specialized cohorts. Here we present BrainFedFM, a structural brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42 federated sites. BrainFedFM uses dual-priority federated pretraining, coupling spatial-priority masking at each site with site-priority aggregation at the server to emphasize informative anatomical regions locally and prioritize site contributions globally. Across 20 downstream datasets spanning 17 classification, regression and segmentation tasks, BrainFedFM achieved the state-of-the-art performance (mean rank 1.68, 50% gain) across seven models, including four centralized foundation models, while showing particularly consistent advantages in classification and regression and robustness across underrepresented populations. These findings demonstrate the generalizability of BrainFedFM and highlight federated pretraining as a practical strategy for developing neuroimaging foundation models from distributed data without pooling raw images.

Zhen Yu, Yang Liu, Xiahai Zhuang +1

National Institute of Health Data Science, Peking University, Beijing, 100871, China. · Institute of Medical Technology, Peking University Health Science Center, Beijing, 100871, China. · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, 100871, China. +2

Medical Imaging Foundation Models Federated Learning Aggregation Federated Learning

Sep 23, 2026cs.CR

Reliable Federated TinyML Deployment for IoT Security

The growing deployment of Internet of Things (IoT) devices has increased the need for privacy-preserving intrusion detection systems that operate directly on resource-constrained hardware. Federated Learning enables collaborative model training without sharing raw data, but conventional federated models are often too large and unstable for deployment on microcontroller-class devices. TinyML techniques enable compact neural networks but are typically designed for inference-only workloads. This work investigates combining Federated Learning with TinyML-based model compression for intrusion detection in IoT environments. We evaluate compression strategies including knowledge distillation, structured pruning, and quantization within a federated training pipeline. Preliminary results show that training stability plays a critical role in federated TinyML systems. In particular, server-coordinated cosine learning-rate scheduling improves Attack Recall from 46.7% to 93.85% while enabling substantial model compression and efficient edge deployment. These findings provide insights for designing lightweight and privacy preserving intrusion detection systems for IoT devices.

Younsoo Park, Seokhyoen Bae, Shasi Kumar Ramachandran Prabhu +2

The Pennsylvania State University, University Park, Pennsylvania, USA · Elizabethtown College, Elizabethtown, Pennsylvania, USA

IoT Intrusion Detection Tiny ML Federated Learning

Sep 22, 2026stat.ML

On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

Federated learning (FL) is intrinsically heterogeneous: honest clients may have different data-generating models. On top of that, adversarial clients can make heterogeneity even more pronounced by sharing arbitrary updates. Existing analyses typically control the interaction between statistical heterogeneity and adversarial behavior through gradient-dissimilarity conditions. However, the underlying bound is imposed a priori and may yield conservative guarantees even for least-squares regression. We instead derive the gradient heterogeneity from the statistical model of linear and nonlinear regression with fresh data samples at every round. Our bounds separate heterogeneity among the honest clients' ground-truth model parameters, finite-sample label noise, and initialization. We then demonstrate that, for any

(f,κ)

-robust aggregator with coefficient

κ= O(f/n)

, where

f

is the number of adversarial clients and

n

the total number of clients (with

f/n < 1/2

), convergence holds after an explicit sample burn-in.

Leonardo F. Toso, James Anderson, Nirupam Gupta +1

Columbia University, USA · University of Copenhagen, Denmark · Sorbonne Université and Université Paris Cité, CNRS, LPSM, France

Byzantine-Robust Federated Learning Non-IID Federated Learning Statistical Learning Theory Federated Learning

Sep 21, 2026cs.LG

A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization

Semi-supervised federated learning (SSFL) trains models on clients' unlabeled data using a teacher to generate pseudo-labels, with a small labeled seed dataset on the server. Automatic Speech Recognition (ASR) is particularly fragile here: pseudo-label errors compound across the output sequence and across training rounds into divergence, leaving a large gap to fully-supervised FL. We show that closing this gap turns on two coupled design axes -- the teacher (which model generates the pseudo-labels) and the anchor (the server-side updates on labeled data that stabilize training). On the teacher axis, a per-client online teacher (each client's own evolving model) diverges on its own, but once stabilized it matches or beats the broadcast global teacher (one server model, fixed within a round) -- decisively in-domain and competitively under domain shift. As the seed grows stronger and the online teacher's advantage narrows, a transitioning teacher (global

\rightarrow

online at round

r

) matches or beats both. On the anchor axis, the server must keep training on labeled data between rounds -- otherwise the online teacher drifts -- and this interleaving, more than the seed model, governs convergence. The two axes are inseparable: aggressive teacher choices pay off only once the anchor stabilizes training, which is highly sensitive to data augmentation and batch size -- the settings that govern how much input and gradient noise the server injects. How much stabilization is needed is domain-dependent, governed by the dispersion of the seed data and its overlap with client data. These findings yield guidelines for SSFL in ASR training, improving over the strongest prior method on 9 of 11 pairs, by

20.8\%

on average in-domain and

10.0\%

cross-domain, narrowing the gap to fully-supervised FL.

Wonho Bae, Zakaria Aldeneh, Martin Pelikan +3

Apple

Automatic Speech Recognition Pseudo-Labeling Federated Learning

Sep 21, 2026cs.LG

A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update

While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predictive performance, it is not always feasible in practice. Federated Learning (FL) architectures have shown to be a very promising approach to use and access distributed disease related resources within the GDPR boundaries. In a previous case report, we described the preconditions at the participating sites and necessary administrative and process related steps to prepare data, people and infrastructure for improving subtype identification and assessing treatment options in pancreatic cancer. We update this report sharing our experience in tackling the challenges and show preliminary results of the actual federated learning AI pipelines. At the participating sites, we have to identify and annotate the data being accessible after extraction and transformation in a local FL hub - in our case a centrally developed and distributively deployed Docker container. This container comprises the FL scripts generating local models. We apply a newly developed FL algorithm considering all local features, including partial overlapping features specific to the local sites. Theoretically, an annotation in a cancer setting should succeed using the German oncology core data set (oBDS), which is already utilized for mandatory reporting to cancer registries, and can be sustained in the FL setting. The FL algorithms deal robustly with partially overlapping features as we showed with public data sets. Major roadblocks including straightening operational concepts for the infrastructures, ethics approval for such novel architectures and support for every site have been addressed. However, scaling up this approach in the future faces hurdles; while including broader multi-modal data sets should be feasible, large-scale deployment to more sites remains challenging.

Anne-Christin Hauschild, Amirreza Aleyasin, Nils H. Beyer +12

University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany · University of Göttingen, Campus Institute Data Science (CIDAS), Section of Medical Data Science, Göttingen, Germany · Institute for Predictive Deep Learning for Medicine and Healthcare, Justus-Liebig University, Gießen, Germany +13

Clinical Decision Support Federated Learning Heterogeneous Federated Learning

Sep 21, 2026cs.CV

FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation

Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated datasets. Privacy constraints further prevent institutions from sharing patient data to overcome this limitation. Moreover, due to the labor-intensive nature of annotation and the scarcity of diverse expertise, institutions typically have labels for only a small portion of their local data, leaving the larger unlabeled portion unused. In this work, we propose a flexible semi-supervised federated multi-task student-teacher framework that leverages federated learning (FL) to improve multi-organ segmentation using both labeled and unlabeled data across participating sites. At each communication round, the proposed framework initiates local training, where clients with labels for the same task form a federation to produce an aggregated teacher model. The resulting teachers generate task-specific features for all data at each client. Subsequently, all clients form a second federation to train a multi-task student model with a shared encoder and task-specific decoders that replicate the teacher-generated features across all segmentation tasks. The aggregated student model is then used to update the local teachers and initiate the next training round. Extensive experiments demonstrated the effectiveness of the proposed method compared with local and federated single-organ models, yielding an average performance gain of 13 percent across clients. The experiments also demonstrated the impact of multi-task learning and unlabeled data and the applicability of the framework in relaxing labeled-data requirements for client participation. The code is available at https://github.com/AshknMrd/FedMust.

Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot

Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway · Central Staff at St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Multi-Task Learning Semi-Supervised Medical Image Segmentation Medical Image Segmentation Teacher-Student Learning Federated Learning

Sep 20, 2026cs.LG

Adaptive Determinantal Client Scheduling in Federated Learning

Scheduling clients for model training is critical in federated learning due to both data and system heterogeneity. Most previous works focus on the quality of the scheduled clients to achieve faster convergence, shorter wall-clock convergence time, or better average model performance. They rarely consider the diversity of clients, which is important to counter heterogeneity and improve performance for the worst-off clients. In this work, we advocate the use of determinantal point processes (DPPs) to model and enhance the diversity in client scheduling. We first design the kernel matrices of DPPs using gradient information and quality scores, which inherently enables a flexible quality-diversity trade-off. Applying fast MAP inference over DPPs, we propose Adaptive Determinantal Client Scheduling (ADCS) in FL. We further quantify the gradient approximation error of ADCS and develop convergence analysis for general biased client selection in FL with non-convex loss functions. We conduct comparative numerical experiments showing that ADCS outperforms state-of-the-art client scheduling algorithms, including both quality-based and diversity-based ones.

Wen Xu, Ben Liang, Gary Boudreau +1

University of Toronto, Canada · Ericsson, Canada

Client Selection in Federated Learning Federated Learning Heterogeneous Federated Learning

Sep 20, 2026cs.CL

Federated Multilingual Speech-LLMs: Architecture and Aggregation Strategy Benchmarking

We present a comprehensive benchmark of Federated Learning (FL) for multilingual Automatic Speech Recognition (ASR), evaluating four Speech-LLM architectures on the Multilingual LibriSpeech dataset. We compare FedAvg and FedProx across frozen and unfrozen encoder configurations, demonstrating that optimized learning rates are critical for performance. Specifically, independently tuning the learning rates for the speech encoder, connector, and decoder yields the lowest error rates, with full three-component adaptation (LoRA for encoder and decoder, full training for the connector) producing the best FL results. We observe that FedProx efficacy is architecture-dependent, providing notable advantages in multilingual pre-trained architectures (e.g., EuroLLM over TinyLlama when keeping the encoder fixed); this indicates that LLM backbone capacity plays a key role in mediating resilience to heterogeneous data distributions. These findings offer concrete design guidance for deploying multilingual Speech-LLMs in privacy-sensitive, distributed environments.

Jordi Luque, Aleix Sant, Fernando López

Telefónica Innovación Digital, Scientific Research

Speech Language Models Federated Learning Multilingual ASR

Sep 20, 2026cs.DC

SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning

Decentralised federated learning replaces server aggregation with peer-to-peer model exchange, making collaborator selection a local decision under uncertainty. Fixed probe budgets waste effort on easy choices yet fall short when peers are hard to distinguish. We propose SAGE (Sequential Anchor-Gated Exchange), an optimal-stopping peer selector under a one-model-bearing-exchange budget. A receiver scores candidate neighbours on receiver-owned anchor evidence and selects once an advantage is certified. It continues probing only while further evidence repays its cost, and otherwise falls back to random gossip. We show that the stopping problem admits an optimal rule attained at a finite stage, and that the anchor schedule is order-optimal in the peer-risk gap and the confidence level. We further show that the selector never returns a peer worse than random gossip with high probability, and prove that no such guarantee holds for selectors that commit without a certificate. A separability threshold follows, below which no probing budget improves on gossip. Experiments span two image benchmarks, two graph families and three heterogeneity levels. Selectors that always act on their evidence lose to gossip in every configuration tested. SAGE-OS matches gossip on 75.5% less evidence than a fixed budget, at half the communication overhead of two published selectors. The operative decision is not which peer to rank first, but whether the evidence justifies ranking at all.

Ke Xiao, Qiyuan Wang, Christos Anagnostopoulos

School of Computing Science, University of Glasgow, Glasgow, UK

Optimal Stopping Adaptive Sampling Federated Learning

Sep 20, 2026cs.LG

Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study

Residential traffic classification supports service management, but learning across homes must account for heterogeneous traffic and privacy constraints. Privacy-aware training may impose uneven costs across traffic categories. We study this tradeoff in simulated two-client federated learning using 1.62 million preprocessed gateway-collected flows across six categories. We compare a full-feature baseline, feature suppression (FS), and differentially private stochastic gradient descent (DP-SGD) under one fixed record-level privacy setting. FS-mild excludes four timing features from 16 model inputs; it provides no formal privacy guarantee. With size-proportional aggregation, FS-mild achieves higher combined macro-F1 and worst-group F1 (the minimum per-class F1 across homes) than DP-SGD in all five seeds at both model capacities under stratified and temporal splits. The tested DP-SGD configuration incurs pronounced minority-category losses, especially in the smaller home, but FS-mild does not uniformly improve on the full-feature baseline. On stratified-split models, loss-based and shadow-model membership probes show near-chance aggregate discrimination without a consistent ranking across probes; this does not establish equivalent privacy. These findings support FS as an input-minimization baseline, not a substitute for formal privacy.

Márton Pál Lipcsey-Magyar, Adrian Pekar

Budapest University of Technology and Economics, Hungary · CUJO LLC Hungary

Differentially Private Stochastic Gradient Descent Federated Learning