Federated Learning Aggregation

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26 papers in the last four weeks, up 117% on the four weeks before. 0.3% of all new papers.

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Latest papers 167

Jun 12, 2026cs.LG

Federated Learning for Feature Generalization with Convex Constraints

Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges, we propose FedCONST, an approach that adaptively modulates update magnitudes based on the parameter strength of the global model. This prevents over-emphasizing well-learned parameters while reinforcing underdeveloped ones. Specifically, FedCONST employs linear convex constraints to ensure training stability and preserve locally learned generalization capabilities during aggregation. A Gradient Signal to Noise Ratio (GSNR) analysis further validates the effectiveness of FedCONST in enhancing feature transferability and robustness. As a result, FedCONST effectively aligns local and global objectives, mitigating overfitting and promoting stronger generalization across diverse FL environments, achieving state-of-the-art performance.

Dongwon Kim, Donghee Kim, Sung Kuk Shyn +1

Department of Computer Science and Engineering, University of Sungkyunkwan, Suwon, Korea · Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

Federated Learning Aggregation Federated Learning Heterogeneous Federated Learning

Jun 11, 2026cs.SD

Towards Personalized Federated Learning for Dysarthric Speech Recognition

Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all speakers to share the same model components can be suboptimal under such heterogeneity, making personalization a promising direction; however, related research on dysarthric speech remains limited. To this end, this paper explores two aggregation strategies to achieve personalization, including the parameter-based averaging strategy and the embedding-based averaging strategy. Experiments on UASpeech and TORGO show that the proposed methods outperform the baseline regularized FedAvg by statistically significant WER reductions of up to 0.99% absolute (3.15% relative) on UASpeech and 0.56% absolute (4.73% relative) on TORGO, respectively.

Tao Zhong, Mengzhe Geng, Jiajun Deng +2

The Chinese University of Hong Kong, China · National Research Council Canada, Canada

Federated Learning Aggregation Automatic Speech Recognition Federated Learning Heterogeneous Federated Learning Dysarthric Speech Recognition

Jun 9, 2026cs.IT

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized federated learning with limited bandwidth and unreliable nodes. Top-K gradient sparsification is an effective approach to reduce communication by transmitting only a few entries of the full gradient, while maintaining competitive model accuracy. Nevertheless, the top-K entries selected by each user are unpredictable and vary across users, which poses a challenge for efficient sparse secure aggregation. This paper studies information-theoretic secure aggregation with top-K sparsification in decentralized federated learning under user dropouts and user collusion. We propose a communication-efficient sparse secure aggregation scheme that offloads dimension-dependent overhead to an offline phase and protects private gradients using random masks and permutations. Experimental results demonstrate that our scheme preserves accuracy comparable to full-gradient aggregation even with only 1% gradient sparsification, while substantially reducing the communication cost.

Hengxuan Tang, Jinbao Zhu, Xiaohu Tang

Southwest Jiaotong University

Federated Learning Aggregation Communication-Efficient Distributed Training Secure Aggregation Federated Learning

Jun 9, 2026cs.LG

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations. Operating over lossy wireless links under constraints, these systems cannot rely on retransmissions, so model parameters must be accepted as partial chunks, leading to two key failure modes, which are selection bias, where poor-quality links are systematically under-represented in gossip aggregation, and update staleness, where asynchronous nodes contribute outdated models. We prove that classical gossip aggregation introduces irreducible selection bias proportional to the link-loss rate. We propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which corrects selection bias using Inverse Probability Weighting (IPW) with online channel estimation and mitigates staleness via Age-of-Information (AoI) decay without requiring a global clock. We prove that DFL-AA removes link-quality distortion in expectation and consistently outperforms state-of-the-art baselines across varying loss rates and heterogeneous channel conditions on fixed directed topologies.

Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya

Quantum Cloud and Distributed Systems (qCLOUDS) Lab School of Computing and Information Systems The University of Melbourne, Australia

Federated Learning Aggregation Asynchronous Federated Learning Age of Information Federated Learning

Jun 8, 2026cs.LG

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning

Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contributing to non-identically distributed data (non-IID). Building on several previous attempts, we define and analyze imbalance issues in FL at three levels: inter-case, inter-class, and inter-client. Inter-case imbalance addresses the imbalance in every single class; inter-class imbalance compares the number of data between different classes. Inter-client imbalance represents different skewness of local data between clients. Based on these concepts, we propose FedBB, which consists of two main components: (1) Positive Negative Balanced (PNB) loss function addresses the inter-case and inter-class imbalances in local training, enhancing generalization on highly skewed local client datasets. It optimizes both multi-label and multi-class classifications by assigning higher weights to minority cases or classes. (2) Client Balanced Reweighting (CBR) reweights clients based on inter-client imbalance during model aggregation, giving greater weight to models trained on less skewed datasets. Various experiments on X-ray and natural image datasets demonstrate that FedBB outperforms other algorithms in both performance and efficiency. Additionally, it requires limited statistical information, which is beneficial for privacy protection. Through ablation studies, we proved that PNB loss and CBR independently contribute to performance. As FedBB aims to build a global model that accurately classifies all classes, it can serve as a baseline for the generic and personalized FL.

Haengbok Chung, Jae Sung Lee

Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea · Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea · Brightonix Imaging Inc. Seoul, Republic of Korea

Class-Imbalanced Learning Federated Learning Aggregation Non-IID Federated Learning Adaptive Loss Weighting Federated Learning

Jun 8, 2026cs.LG

FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching

Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed client participation, the resulting update staleness can become severe enough to destabilize training. To remedy this, we propose FedSteer, a novel method that constructs a gradient subspace from a cache of recent client gradients to serve as a low-dimensional representation of the current optimization landscape. FedSteer projects an active client's true gradient onto this subspace to find a set of optimal coordinates. For an inactive client, FedSteer reuses these coordinates with the now-evolved subspace drifted by other active clients. This process effectively "steers" outdated gradients toward the current global objective. This is complemented by a selective caching strategy that identifies a representative client subset to form the subspace, reducing server memory. Experiments demonstrate that FedSteer significantly outperforms baselines, preventing performance collapse in challenging scenarios while delivering accuracy gains of over 7% in others.

Haoran Zhang, Cainã Figueiredo Pereira, Marie Siew +3

Electrical and Computer Engineering Dept., The University of Texas at Austin, Austin, Texas, USA · CERI/LIA, University of Avignon, Avignon, France · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore +2

Federated Learning Aggregation Federated Learning

Jun 5, 2026cs.IT

The Capacity of Information-Theoretic Secure Aggregation in Federated Learning

Secure aggregation allows a server to aggregate users' local updates while preserving update privacy. Existing information-theoretic problems typically assume that correlated random keys are provided by a trusted third party (TTP) or generated via prescribed groupwise structures, while the communication cost for establishing such correlated keys is often ignored. Consequently, the fundamental limits under general key-distribution mechanisms remain unknown. In this paper, we study the

T

-colluding information-theoretic secure aggregation problem with

N

users under a general two-phase framework consisting of a key distribution phase and an update aggregation phase. Unlike prior work, we model key distribution through user-to-user communication and allow arbitrary user-generated key-distribution mechanisms, eliminating TTP or prescribed structures. This enables a joint characterization of three resources: randomness for security, key-distribution communication, and aggregation communication. We completely characterize the capacity region among these three resources by constructing a novel secure aggregation scheme together with a matching information-theoretic converse. In particular, we develop an explicit deterministic capacity-achieving construction over any finite field of size at least

N

, whereas most existing schemes either rely on TTP or employ randomized or existential constructions over sufficiently large finite fields. We further show that the optimal performance can be achieved using only pairwise shared keys, enabling implementation via Diffie--Hellman key exchange. Compared with Google's seminal secure aggregation scheme, the proposed scheme requires fewer random masking keys while preserving the same aggregation communication overhead.

Lanxin Yi, Jinbao Zhu, Kai Wan +1

Information Coding and Transmission (ICT) Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China · School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Federated Learning Aggregation Privacy-Preserving ML Secure Aggregation Federated Learning

Jun 4, 2026cs.AI

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models

Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA methods suffer from two fundamental limitations: (1) structural aggregation bias, where independently averaging low rank factors fails to approximate the true combined update, and (2) client side initialization lag, as clients repeatedly reinitialize LoRA parameters across communication rounds, slowing convergence. We propose HyperLoRA, a unified framework that addresses both issues through amortized federated adaptation through hypernetwork-driven LoRA generation and product space aggregation. Instead of iterative per-client optimization, HyperLoRA employs a learned generator that maps client distribution signatures to LoRA initializations, effectively amortizing per client adaptation. On the server side, we introduce a learned aggregation module that directly synthesizes updates in the low-rank product space, eliminating the inconsistencies of factor-wise averaging. A lightweight residual correction module further improves stability under heterogenous (non-IID) client distributions.By replacing iterative optimization and heuristic averaging with learned operators, HyperLoRA jointly enables efficient personalization, unbiased aggregation, and faster convergence. Experiments on federated vision and vision-language benchmarks show that HyperLoRA achieves improved convergence speed, greater robustness to distribution shift, and stronger personalization performance compared to prior federated LoRA methods.

Sunny Gupta, Shambhavi Shanker, Amit Sethi

Indian Institute of Technology, Bombay

Federated Learning Aggregation Non-IID Federated Learning Low-Rank Adaptation Hypernetworks Federated Fine-Tuning Federated Learning

Jun 2, 2026cs.LG

FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data

Recent advances in language models have established reinforcement learning as the primary paradigm for eliciting self-correction and long-chain reasoning. While group relative policy optimization (GRPO) offers superior scalability by eliminating the critic network, deploying it on a central infrastructure entails collecting a large volume of data from distributed owners, which poses significant privacy risks. To address these concerns, we introduce federated GRPO (FGRPO), a framework designed to decentralize the fine-tuning of reasoning models across heterogeneous data owners. To effectively mitigate the instability caused by divergent reward scales across heterogeneous tasks, FGRPO incorporates an adaptive aggregation mechanism based on relative performance gain. By characterizing each client's improvement relative to its personalized historical baseline, the framework dynamically prioritizes effective learning trajectories regardless of local task difficulty. FGRPO ensures robust convergence on non-IID data while preserving data privacy.

Pengyu Chen, Shaowei Li, Kai Wang +4

School of Computer Science and Technology, Shandong University, Qingdao China · School of Mathematical Science, Peking University, China · School of Computer Science and Artificial Intelligence, Shanghai University of Finance and Economics, Shanghai, China +1

Federated Learning Aggregation Federated RL RL for Language Model Reasoning Group Relative Policy Optimization Non-IID Federated Learning Federated Learning

Jun 1, 2026cs.LG

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets (

\varepsilon_i

) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ

\varepsilon

-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by

\varepsilon

-aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a \emph{Privacy Inference Attack} that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL

\varepsilon

-aware aggregation. To address this challenge, we propose \textbf{IntraShuffler}, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving

\varepsilon

-aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.

Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun

University of Tennessee, Knoxville, USA · Oak Ridge National Laboratory, USA

Federated Learning Aggregation Privacy-Preserving ML Differentially Private Federated Learning Differential Privacy Heterogeneous 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 28, 2026cs.CR

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data, and limited model interpretability. Federated Learning (FL) offers a privacy-preserving solution; however, existing approaches such as SOH-FL suffer from two key limitations: reliance on a manually tuned aggregation parameter γ and lack of explainability in model predictions. In this paper, we propose XAI-SOH-FL, an enhanced framework that integrates adaptive aggregation and explainable artificial intelligence into the SOH-FL paradigm. First, we introduce a dynamic γ selection mechanism based on similarity thresholding, enabling the aggregation process to adapt to evolving data distributions. Second, Bayesian Optimization is employed to automatically determine optimal γ values, eliminating the need for manual tuning. Third, SHAP (SHapley Additive exPlanations) is incorporated to provide feature-level interpretability for intrusion detection decisions. Experimental evaluation on the CICIDS2017 dataset demonstrates that the proposed approach achieves an accuracy of 94.12% and an F1-score of 0.92, outperforming the baseline SOH-FL model while converging in fewer communication rounds. Furthermore, SHAP-based analysis reveals that flow-level features such as Flow Duration and Packet Length significantly influence model predictions. These results indicate that XAI-SOH-FL provides an effective balance between accuracy, adaptability, and interpretability in heterogeneous IoT environments.

Ambreen Aslam, Maaz Hassan, Bibi Zahra +1

School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Federated Learning Aggregation Network Intrusion Detection Federated Learning Heterogeneous Federated Learning

May 28, 2026cs.LG

Fairness-Aware Federated Learning with Trajectory Shapley Value

Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple clients to train a model collaboratively by aggregating their local updates at a server. However, conventional aggregation schemes typically use fixed weights that fail to reflect unequal and time-varying client contributions, leading to biased and unstable learning. To improve fairness and stability, we propose the Trajectory Shapley Value (TSV), a contribution metric that evaluates how each client influences the optimization trajectory of the global model using a validation-based, temporally consistent utility. Building on TSV, we design FedTSV, an adaptive aggregation method that converts per-round evaluations into dynamic client weights, allowing the server to respond to heterogeneous and adversarial participation in real time. Experiments on benchmark datasets show that FedTSV accelerates convergence, improves robustness, and yields more equitable contribution assessments, thereby providing a principled foundation for fairness-aware federated optimization.

Daniel Kuznetsov, Ziqi Wang

Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay, 4 Av. des Sciences, 91190 Gif-sur-Yvette, France. · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany.

Federated Learning Aggregation Algorithmic Fairness Shapley Value Attribution Federated Learning Heterogeneous Federated Learning

May 28, 2026cs.CR

Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels

Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption. Existing HE-over-the-air (OTA) methods mainly rely on single-key HE schemes and require channel estimation or pre-equalization to compensate for wireless fading. However, single-key HE remains vulnerable to honest-but-curious (HBC) clients holding the shared secret key, while multi-key HE provides stronger client-level security by assigning each device its own secret key. We propose a four-phase protocol that enables the aggregation of xMK-CKKS over a shared wireless channel without channel estimation. The protocol retransmits partial public keys and ciphertexts through the same channel realization, so that the dominant large-modulus encryption terms cancel algebraically during decryption. We integrate this protocol with zero-order FL over slowly varying LoS-dominant channels, where each device transmits a single encrypted scalar per round and the communication/encryption overhead is independent of the model dimension. We show that the residual noise induced by encryption and wireless aggregation preserves the standard convergence rate

O(1/\sqrt{K})

up to a negligible noise floor, where

K

is the number of communication rounds. The protocol assumes a non-trusted server and is secure against HBC clients, preventing any client from recovering the local updates of other participants. Numerical results on MNIST and CIFAR-10 validate the theoretical analysis.

Anthony Ayli, Khalil Harris, Jihad Fahs +1

Federated Learning Aggregation Homomorphic Encryption Privacy-Preserving ML Over-the-Air Federated Learning Federated Learning

May 28, 2026cs.LG

Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction

Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low communication overhead, Federated Averaging (FedAvg) is the standard aggregation choice in QFL literature. However, deploying QFL on practical hardware exposes a severe double-drift phenomenon: the global model is simultaneously derailed by client drift from non-IID data and hardware bias from noisy quantum gradient estimates. In this work, we first analyze the convergence of FedAvg under these realistic conditions, mathematically demonstrating that quantum hardware bias creates a persistent error floor that standard averaging cannot correct. To overcome this limitation, we propose Q-ANCHOR, a quantum-aware federated aggregation architecture that anchors server updates with zero-noise extrapolation while applying stateful client correction to suppress both client drift and hardware-induced bias. Our convergence theory proves that Q-ANCHOR successfully mitigates classical client drift while actively reducing the hardware-bias floor. Experimental results demonstrate that Q-ANCHOR achieves significantly more stable training than conventional FL baselines.

Hoang M. Ngo, Quan Nguyen, Wanli Xing +1

Department of Computer & Information Science & Engineering University of Florida · Frost Institute for Data Science and Computing University of Miami

Federated Learning Aggregation Quantum Machine Learning Non-IID Federated Learning Quantum Error Mitigation Federated Learning

May 28, 2026stat.ML

Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets

In federated language modeling,

K

nodes each hold

n

samples but cannot pool data or exchange full-precision gradients or weights. We study the minimax rate at which a conditional distribution over

V

tokens can be estimated when each node may upload at most

B

bits per query in a public probe set. In federated probe-logit distillation (FPLD), each node transmits a scalar-quantized logit vector on the probe set, and an aggregator distills a global parametric student. Prior work (Dubey and Huo, 2026) establishes a high-probability KL rate

O(d/(Kn) + ρ\sqrt{V \log V / m} + K^{-1} \cdot 2^{-2B/V})

plus optimization slack, with the bandwidth term in its trace-sharpened form. Whether this bandwidth-term rate is tight, and how the upper bound generalizes to heterogeneous per-node bandwidths, are left open. We close both gaps. First, the dithered FPLD construction has a matching single-round lower bound

Ω(K^{-1} \cdot 2^{-2B/V})

under non-degeneracy, pinning the bandwidth-axis rate at

Θ(K^{-1} \cdot 2^{-2B/V})

.

T

-round sequential refinement with nested/scaled residual quantizers achieves

O(K^{-1} \cdot 2^{-2TB/V})

; vanilla FPLD's

T

-independent bandwidth term is suboptimal for every

T > 1

. Second, we establish a heterogeneous-bandwidth upper bound for per-node budgets

B_i

, paired with a closed-form optimal allocation

B_i^* = B_{\mathrm{tot}}/K + (V/2) \log_2(w_i / \bar{w}_g)

, a log-tilted water-filling rule that is the per-node analogue of reverse water-filling for distortion-rate optimization. A plug-in adaptive variant estimates the weights from a short warm-up phase and attains

1 + O(\sqrt{\log(K/δ)/(m T_0)})

relative suboptimality. Synthetic n-gram simulations confirm that empirical KL is bracketed by the upper and lower bounds and that the optimal allocation strictly dominates uniform and inverse-weighted baselines under heterogeneous clipping.

Prasanjit Dubey, Xiaoming Huo

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.

Federated Learning Aggregation Communication-Efficient Distributed Training Federated Knowledge Distillation Rate-Distortion Optimization Knowledge Distillation Heterogeneous Federated Learning

May 27, 2026cs.LG

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning

Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style parameter averaging is not function-space consistent: when clients use heterogeneous encoders or even identical nonlinear networks, averaged parameters need not correspond to the weighted average of client value functions in any common function space. We propose FedQHD, a federated Q-learning method using hyperdimensional (random-feature) state encoders with a linear readout, so that Q-functions are nonlinear in state yet linear in trainable parameters. This linear structure enables closed-form aggregation. With a shared encoder, the function-space consensus update coincides exactly with weighted averaging of local readout matrices. With heterogeneous encoders, the server constructs a global teacher by averaging client Q-values on a shared anchor-state set, and each client compiles this teacher into its local representation via a single ridge projection. We formalize the federation gap -- the error incurred when compiling a federated teacher into a heterogeneous client representation -- relative to a client-specific oracle projection. We show that this gap decomposes into subspace misalignment, anchor-set conditioning, and regularization bias. We further identify the anchor-to-dimension ratio

m \geq D_i

as the well-conditioned regime in which the gap reduces to a multiple of the encoder heterogeneity floor. On four continuous-state, discrete-action control benchmarks, FedQHD matches or outperforms FedAvg-style baselines and distillation-based alternatives while requiring substantially less computation, and the empirical dependence of the federation gap on encoder dimension matches our theoretical analysis.

Yuchen Hou, Yongshan Chen, Zhuowen Zou +4

Northeastern University · University of California, Irvine · The George Washington University

RL Control Federated Learning Aggregation Federated RL Federated Learning Heterogeneous Federated Learning

May 27, 2026cs.LG

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee

Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approaches can neutralize these threats but incur substantial computational overhead during high-dimensional gradient aggregation, an overhead that scales poorly with model size and increasingly dominates the training cost as modern models grow larger. To address this computational bottleneck, we propose Projected Dimensionality Reduction (PDR), a universal acceleration framework for vector-level distance-based robust aggregators, which performs robust aggregation by compressing gradients into a drastically smaller subspace via sparse random projection to efficiently compute reliability weights. This approach reduces the server computational complexity to an optimal

\mathcal{O}(Mp)

, where

M

is the number of clients and

p

is the model dimension, matching the theoretical lower bound required merely to read the gradients. We establish convergence guarantees under standard FL assumptions in prior Byzantine-robust FL analyses. By leveraging the Subspace Embedding Theorem, we show that PDR achieves optimal convergence rates of

\mathcal{O}(1/\sqrt{T})

for non-convex functions and

\mathcal{O}(1/T)

for strongly convex functions, where

T

denotes the number of iterations. Crucially, we mathematically demonstrate that this massive acceleration comes almost for free, merely inflating the inherent Byzantine error floor by a bounded, tunable factor of

\frac{1+ε}{1-ε}

. Experimental results on benchmark datasets confirm that integrating PDR with existing aggregators yields orders of magnitude speedups in time efficiency while maintaining highly competitive convergence performance.

Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2

Beijing Institute of Technology, Beijing, China · Xi’an Jiaotong University, Xi’an, China · The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

Federated Learning Aggregation Byzantine-Robust Federated Learning Dimensionality Reduction

May 26, 2026cs.LG

Separate Aggregation of Split Network for Personalized Federated Learning

Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous client data distributions. A single global model often cannot satisfy diverse client requirements, so personalized federated learning has therefore been explored to improve client specific performance while preserving global generalization. Existing PFL methods often face a fundamental tradeoff in which stronger global sharing can undermine local specialization, whereas stronger local adaptation can lead to overfitting under limited data, label imbalance, and missing class scenarios. In this work, we propose PGFedSplit, a personalized federated learning framework that improves both personalization and global generalization under severe client heterogeneity. PGFedSplit adopts a split architecture and performs adaptive aggregation scheduling tailored to the roles of different model components, enabling stable knowledge sharing while maintaining client specific adaptation. Each client further leverages a mixture of locally extracted representations and synthetic representations generated from server side Gaussian statistics, improving robustness under label imbalance and missing class conditions. Extensive experiments on Fashion MNIST, CIFAR 10, CIFAR 100, and Tiny ImageNet demonstrate consistent improvements over state of the art PFL methods, with stable convergence and superior personalization in highly heterogeneous settings.

Yunseok Kang, Jaeyoung Song

Department of Electronics Engineering, Pusan National University, Republic of Korea

Federated Learning Aggregation Split Federated Learning Federated Learning Heterogeneous Federated Learning

May 24, 2026cs.LG

On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach

Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale and heterogeneous systems. However, frequent peer-to-peer communication, asynchronous updates on directed topologies, and non-IID data jointly lead to excessive communication overhead, biased aggregation and severe model drift. We propose PushCen-ADFL, a communication-efficient ADFL framework that enables stable training under asymmetric communication and delayed client participation. PushCen-ADFL couples communication, aggregation, and local stabilization in a shared centroid representation space, forming a closed loop between compression and optimization. Clients exchange centroid-form messages, apply average-preserving push-sum mixing to correct aggregation bias, and use a lightweight centroid regularization anchored in the same centroid space to mitigate drift under heterogeneity and staleness. A bounded, sender-deduplicated buffer further improves robustness under irregular asynchronous arrivals. Experiments on vision datasets demonstrate that PushCen-ADFL improves accuracy under data heterogeneity by up to 6% while reducing per-push communication cost by more than 80%, achieving a favorable accuracy-communication trade-off.

Jiahui Bai, Hai Dong, A. K. Qin

School of Computer Technologies, RMIT University Melbourne, VIC, Australia · School of Science, Computing and Engineering Technologies, Swinburne University of Technology Hawthorn, VIC, Australia

Multi-Agent Coordination Federated Learning Aggregation Communication-Efficient Distributed Training Asynchronous Federated Learning Non-IID Federated Learning Federated Learning

May 23, 2026cs.LG

ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning

Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and centralized aggregation, which fails when hospitals have unequal compute resources. We propose capacity-aware coordination: measure each hospital's throughput, assign capacity-appropriate architectures (MobileNetV3-Small, EfficientNet-B0, ResNet-50), and combine predictions via weighted ensemble. Weak and strong hospitals can participate without forcing uniform architectures. We separate on-chain policy from off-chain learning. A Solidity contract stores hospital registration, benchmark hashes, metrics, and weights. Hospitals train locally and submit only hashes and scalars (not parameters). Weighted ensemble inference is computed off-chain. Experiments on PneumoniaMNIST and DermaMNIST (5 seeds, 3 non-IID levels) show our method achieves lower or equal calibration error versus equal-weight ensemble and competitive accuracy versus FedAvg, FedProx, and FedMD. Communication overhead is 224 bytes per round, a reduction of over 912,000x compared to FedAvg.

Karan Sharma, Aditya Tripathi, Rahul Mishra +1

DA-IICT Gandhinagar, India · IIT Patna Patna, India

Federated Learning Aggregation Federated Learning Heterogeneous Federated Learning

May 21, 2026cs.LG

FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling patient-level clinical and genomic data across institutions. In practice, deployment is further complicated by feature-space heterogeneity, in which sites collect different covariates or use different sequencing panels, resulting in only partially overlapping feature sets. We present FederatedRSF, a Python package that implements federated random survival forests, aggregating locally trained survival trees and redistributing only feature-compatible trees to each site, enabling inference with partial overlap without sharing raw data. We evaluate FederatedRSF on the GBSG2 breast cancer cohort distributed with the scikit-survival package, simulating feature heterogeneity across clients by withholding subsets of features, and assessing discrimination using Harrell's concordance index (C-Index) under repeated cross-validation and site-splits. The results demonstrated that the federated model can achieve performance comparable to that of the centralized training setting.

Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin +3

Institute for Predictive Deep Learning in Medicine and Healthcare, Justus Liebig University Gießen, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Department of Medical Informatics, University Medical Center Göttingen, Germany +1

Federated Learning Aggregation Cancer Survival Prediction Healthcare Survival Analysis Privacy-Preserving ML Federated Learning+1

May 21, 2026cs.LG

FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning

Federated learning protocols face a structural trilemma: canonical server-based aggregation~\cite{mcmahan2017} creates a single point of failure and gradient inversion risk; decentralised ring-gossip alternatives~\cite{hu2019segmented} expose classification heads to semi-honest peers via uninformed uniform weights; and personalised methods~\cite{collins2021exploiting} reintroduce central aggregation. No existing protocol simultaneously achieves server-free operation, permanently private heads, ring topology, and principled asymmetric neighbour weighting. We propose FIRMA (\textbf{FI}bonacci \textbf{R}ing \textbf{M}odel \textbf{A}ggregation), a family of three progressively enhanced federated learning protocols: 1) \fibfl\ establishes the foundation: server-free ring aggregation with Fibonacci-weighted neighbour blending and permanently private classification heads. 2) \fibflp\ augments this with accuracy-gated neighbour suppression, selectively down-weighting poorly-converged peers while preserving the Fibonacci directional bias. 3) \fibflpp, the full system, completes the family with a 2-opt ring permutation that maximises adjacent-client class diversity, global ring coverage via

K_g{=}\lceil N/2\rceil

gossip passes, and cosine-annealed self-retention calibration. We establish a convergence rate bound and three supporting propositions governing normalisation, coverage, retention, and diversity optimality. Systematic experiments across 28 configurations -- four benchmarks crossed with seven heterogeneity regimes -- demonstrate that \fibflpp\ surpasses \fedavg\ in all 12 label-skew configurations, with a peak advantage of

+20.7

,pp on CIFAR-10 at

K{=}1

. Under Dirichlet heterogeneity, \fibflpp\ is the Pareto-dominant method among all server-free protocols, achieving the highest accuracy in 17 of 28 configurations.

Rachid Hedjam

Department of Computer Science Bishop’s University Sherbrooke, Qc, Canada

Federated Learning Aggregation Non-IID Federated Learning Privacy-Preserving ML Decentralized Optimization Federated Learning

May 21, 2026cs.CR

EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning

Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typically rely on fixed thresholds or perform clustering with a fixed number of clusters to distinguish malicious gradients from benign ones. However, these methods are difficult to adapt to dynamic poisoning strategies of malicious clients, and often result in the loss of benign gradients due to the heterogeneity of clients' local datasets. To address these problems, we propose a novel robust aggregation method that leverages a small number of known benign clients as references, enabling accurate identification and filtering of malicious gradients while retaining as many benign gradients as possible, even when the number of malicious clients is unknown and variable. First, we introduce a density-based low-dimensional gradient clustering method, which projects gradients onto the two most divergent dimensions and applies density-based clustering to identify malicious gradients while retaining clustered benign gradients and potentially benign outliers. Second, we design an enhancing clustering low-dimensional gradient generator model, which learns to generate pseudo-gradients aligned with the boundary of the benign cluster. These pseudo-gradients act as bridges to connect sparse benign gradient outliers. Third, we introduce low-dimensional gradient re-clustering that clusters the generated pseudo-gradients together with real gradients to recover benign gradients misclassified as noise points, enabling more benign gradients to participate in aggregation. Extensive experiments on the MNIST, CIFAR-10, and MIND datasets demonstrate that our method exhibits superior fidelity and robustness under dynamic poisoning scenarios.

Tianyun Zhang, Zhen Yang, Haozhao Wang +2

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Federated Learning Aggregation Byzantine-Robust Federated Learning Adversarial Attacks Clustering Federated Learning

May 21, 2026cs.LG

Detecting Atypical Clients in Federated Learning via Representation-Level Divergence

Federated learning enables collaborative training across distributed clients with heterogeneous data, but such heterogeneity often leads to unstable updates and degraded global performance. Moreover, in practical deployments, client updates may deviate from the expected behavior not only due to benign not i.i.d. distributions, but also due to distributional shifts or anomalous inputs, raising concerns about the reliability of the aggregation process. In this work, we propose a lightweight geometric signal to quantify the functional deviation of a client with respect to the global model. Instead of comparing model parameters or gradients, our approach measures how the local training of each client alters the activation-induced partition of the input space, evaluated on a shared probe set. This yields a permutation-invariant, interpretable metric of client--global divergence that captures differences in how data is processed by the model. We show that this signal effectively identifies clients that induce atypical functional changes, distinguishing stable yet heterogeneous clients from those whose updates significantly diverge from the global regime. As a result, the proposed metric provides a simple tool for monitoring client behavior and enabling risk-aware aggregation strategies in federated learning systems.

Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2

Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain

Federated Learning Aggregation Non-IID Federated Learning Federated Learning Heterogeneous Federated Learning Anomaly Detection

May 20, 2026cs.LG

Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction

Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count (FBC) data and deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT), two clinical environments that differ markedly in iron deficiency prevalence, ferritin distribution, and subject populations. A frozen domain-specific haematology foundation model, DeepCBC, performs site-local representation extraction, restricting federated training to a compact downstream classifier and substantially reducing recurrent communication relative to full-encoder federation. The two clinical datasets are structurally not independent and identically distributed (non-IID), with heterogeneity arising from distinct population differences rather than sampling artefacts. Runtime governance is enforced by FLA

^3

, a healthcare-oriented FL platform providing study-scoped execution, policy-based authorisation, and signed audit logging. Standard sample-size-weighted aggregation (FedAvg) reduced the area under the receiver operating characteristic curve (ROC-AUC) at both sites relative to local-only training, as the global update was biased towards the larger AUMC distribution. FedMAP, a personalised aggregation method, raised ROC-AUC from 0.9470 to 0.9594 at AUMC and from 0.8558 to 0.8671 at NHSBT relative to local-only training, achieving the highest macro ROC-AUC of 0.9133 and the best macro balanced accuracy overall. These results support personalised aggregation in clinical federations where client sample size and task relevance diverge substantially.

Fan Zhang, Simon Deltadahl, Majid Lotfian Delouee +10

Dept. of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge, UK · Translational AI Laboratory, Dept. of Laboratory Medicine Amsterdam UMC Amsterdam, The Netherlands · Precision Health University Research Institute Queen Mary Univ. of London London, UK +3

Federated Learning Aggregation Healthcare Non-IID Federated Learning Clinical Prediction Federated Learning

May 20, 2026cs.LG

CRAFT: Conflict-Resolved Aggregation for Federated Training

The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a global update that improves the global objective while conflicting with specific clients, causing degradation for those clients. In this work, we propose CRAFT (Conflict-Resolved Aggregation for Federated Training), a new aggregation framework that treats the global update as a geometric correction problem. We formulate aggregation as finding the update closest to a reference direction while satisfying conflict-free alignment constraints. We derive a closed-form expression for the constrained optimization problem, avoiding the computational overhead of iterative solvers. Furthermore, we use a layer-wise adaptation to address conflicts at varying feature granularities. We provide a theoretical analysis showing that CRAFT promotes a common-descent structure and mitigates conflicts through its projection geometry. Extensive experiments on heterogeneous benchmarks demonstrate that CRAFT improves the accuracy of the global model while reducing performance disparity across clients compared with state-of-the-art baselines. The source code for CRAFT is available at https://github.com/tum-pbs/CRAFT.

Ziqi Wang, Qiang Liu, Nils Thuerey

Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. · School of Computation, Information and Technology, Technical University of Munich, Garching, Germany. · Munich Center for Machine Learning, Munich, Germany.

Federated Learning Aggregation Gradient Interference Federated Learning Heterogeneous Federated Learning

May 20, 2026cs.LG

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed learning. However, existing FL methods face a fundamental challenge. Traditional averaging-based approaches suffer from parameter divergence under non-IID conditions, while personalized FL methods overfit to local data and fail to generalize to new clients (cold-start problem). Mixture-of-Experts naturally addresses this by routing heterogeneous data to specialized experts rather than forcing uniform aggregation. In this paper, we propose FedCoE, a Federated Coordinated dual-level mixture-of-Experts framework that effectively balances global generalization with local personalization. FedCoE maintains multiple independent global expert models on the server and employs a shared gating network to dynamically model client-expert correlations during aggregation, effectively mitigating expert drift and gating inconsistency. To address the cold-start challenge, we introduce an adaptive mechanism that enables new clients to immediately leverage the global expert pool without extensive local training. Extensive experiments demonstrate that FedCoE achieves 78.00% global accuracy and 89.32% personalized accuracy on average, outperforming the baseline by 8.82% and 29.19%, respectively. In cold-start scenarios, FedCoE delivers 77.27% accuracy without any local fine-tuning, outperforming baselines by over 12.54%.

Penglin Dai, Fulian Li, Xincao Xu +3

School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Federated Learning Aggregation Non-IID Federated Learning Mixture-of-Experts Models Federated Learning

May 20, 2026cs.LG

A Typed Tensor Language for Shared-State Federated Computation

Shared-state federated computations combine client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor language for this class of computations. Its two tensor sorts separate client-partitioned data from globally available values, and typing tracks the partitioned axis. A virtual global tensor serves as a semantic reference for centralized evaluation. We show that typed one-round programs factor through shared tensors whose shapes depend on the program but are independent of client and sample counts. The converse applies to typed-realizable factorizations: each encoder component is represented by an allowed aggregation or contraction with its valid merge, and the decoder is shared-only. The construction extends round by round to programs whose persistent state is shared. For a loss supplied with a client-local per-sample gradient expression, summation represents the empirical gradient. This gives typed programs for server-side first-order updates and, with shared linear algebra, curvature-block updates. The language covers federated analytics and FedSGD. General multi-local-step FedAvg and persistent private client state are outside its scope.

Theofilos Mailis, Theodore Papamarkou, Andreas Ktenidis +6

Athena Research Center · National and Kapodistrian University of Athens · National Technical University of Athens

Federated Learning Aggregation Federated Learning

May 18, 2026stat.ML

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning

Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-offs among estimation accuracy, privacy constraints, and communication cost for differentially private (DP) federated M estimation. The two standard methods in the literature are FedAvg, which may suffer from high federation bias, and FedSGD, which can incur high communication cost. Aimed at improving accuracy at a reduced communication cost, we propose FedHybrid, which uses FedSGD starting with an improved initialization by the FedAvg estimator. We propose FedNewton, which averages local Newton iterations to reduce bias in FedAvg, achieving an estimation accuracy comparable to FedSGD with much fewer communication rounds when the number of clients grows sufficiently slowly. We establish finite sample upper bounds on the mean-squared error rates of the DP versions of these estimators as functions of the number of clients, local sample sizes, privacy budget, and number of iterations. We further derive a minimax lower bound on the MSE of any iterative private federated procedure that provides a benchmark to assess the optimality gap of these methods. We numerically evaluate our methods for training a logistic regression and a neural network on the computer vision datasets MNIST and CIFAR-10.

Arnab Auddy, Xiangni Peng, Subhadeep Paul

Department of Statistics · The Ohio State University

Federated Learning Aggregation Communication-Efficient Distributed Training Differentially Private Federated Learning Statistical Learning Theory Federated Learning