Federated Learning

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Federated learning studies how multiple devices or institutions can train models collaboratively while keeping data local, for example across hospitals, phones, or companies. A common approach, FedAvg, aggregates local model updates; related methods use personalization or client clustering, split learning, model compression, and lightweight adapters such as LoRA. Differential privacy and secure aggregation can limit exposure of updates, while robust aggregation and client-selection methods address malicious participants and uneven data or resources. The central challenge is balancing accuracy and fairness across heterogeneous clients with privacy, security, communication cost, and reliable evaluation; keeping data local alone does not guarantee privacy or strong performance.

Latest papers

963 papers112 papers in the last four weeks, up 1% on the four weeks before. 0.6% of all new papers.

Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
May 8, 2026Ozgu Goksu, Nicolas PugeaultRepresentation Collapse Non-IID Federated Learning
School of Computing Science University of Glasgow
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
May 8, 2026Usevalad Milasheuski, Piero Baraldi, Enrico Zio +1Communication-Efficient Distributed Training Predictive Maintenance
Consiglio Nazionale delle Ricerche, IEIIT institute, 20133 Milan, Italy · Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, Italy · Department of Energy, Politecnico di Milano, 20156 Milan, Italy +1
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
May 8, 2026Yuhao Zhou, Yunpeng Zhu, Yang Zhou +7Vision-Language Models Efficient VLA Models
Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education · Zhejiang University +2
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Multimodal Federated Learning Federated Learning Aggregation
National Taiwan University · RIKEN-CCS · The University of Osaka +1
Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
May 8, 2026Hao Chen, Zavareh BozorgaslFederated Learning Aggregation Wireless Communications
Signal, Communication, and Learning Lab (SCALE Lab), Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83712 USA
Modulated learning for private and distributed regression with just a single sample per client device
May 8, 2026Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1Differentially Private Federated Learning Differential Privacy
Massachusetts Institute of Technology (MIT) · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
May 8, 2026Hong Chen, Pengcheng Wu, Yuanguo Lin +4Test-Time Adaptation Non-IID Federated Learning
Nanyang Technological University · Jimei University · Xiamen University
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Meta-Learning OOD Generalization
Department of ECE and INMC, Seoul National University, Seoul, South Korea · Graduate School of Data Science, Seoul National University, Seoul, South Korea · Department of Computer Science, University of Colorado Boulder, CO, USA
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
May 7, 2026Mianyong Ding, Maximilian Knoll, Semi Harrabi +7Medical Image Segmentation Oncology
Department of Radiation Oncology, Imaging and Cancer Division, University Medical Centre Utrecht, Utrecht, The Netherlands · University Hospital Heidelberg, Heidelberg Ion Beam Therapy Centre (HIT), Department of Radiation Oncology, Heidelberg, Germany · Princess Máxima Centre for Pediatric Oncology, Utrecht, The Netherlands +3
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
May 7, 2026Su Zhang, Junfeng Guo, Heng HuangPrivacy-Preserving Language Models LLM Fine-Tuning
Department of Computer Science, University of Maryland, College Park
CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
May 7, 2026Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2IoT Intrusion Detection Network Intrusion Detection
Yale University, New Haven, CT, USA · Nokia Bell Labs, Murray Hill, NJ, USA
FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing
May 7, 2026Junye Du, Zhenghao Li, Yushi Feng +1Transformer Attention Federated Learning
The University of Hong Kong
Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detection
May 7, 2026Selin Ceydeli, Rui Wang, Kubilay AtasuGraph Neural Networks Federated Graph Learning
Delft University of Technology, Department of Software Technology Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
May 7, 2026Wentao Yu, Bo Han, Jie Yang +1Federated Graph Learning Manifold Learning
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, China · Department of Computer Science Hong Kong Baptist University Hong Kong, China · School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai, China
Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
May 7, 2026Jinqian Chen, Chang Liu, Jihua ZhuFederated Learning Aggregation Low-Rank Adaptation
School of Software Engineering Xi’an Jiaotong University Xi’an, China 710000
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
May 7, 2026Xinghao Wu, Jianwei Niu, Guogang Zhu +3Representation Alignment Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100091, China · Zhongguancun Laboratory, Beijing 100194, China · Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo, NY 14260 USA
VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
May 7, 2026Mohamed Lakas, Mohamed Amine FerragIoT Intrusion Detection Client Selection in Federated Learning
College of Information Technology, United Arab Emirates University, UAE
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
May 7, 2026Myoungjun Kim, Sangwoo Park, Yoseob Han +1Differentially Private Federated Learning Federated Learning
Myongji University · King’s College London · Soongsil University
Enabling Federated Inference via Unsupervised Consensus Embedding
May 7, 2026Yui Hashimoto, Takayuki Nishio, Yuichi Kitagawa +1Ensemble Learning Privacy-Preserving ML
School of Engineering, Institute of Science Tokyo, Tokyo 152-8550, Japan · Research & Development Group, Hitachi Ltd., Tokyo 185-8601, Japan
FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings
May 7, 2026Quang-Huy Nguyen, Jiaqi Wang, Wei-shinn KuFederated Knowledge Distillation Knowledge Distillation
Department of Computer Science and Software Engineering Auburn University Auburn, AL 36849
A Simulated Federated Analysis of MS-Induced Brain Lesions
May 6, 2026Evelyn Trautmann, Joël Federer-Gsponer, Markus C. Elze +1Synthetic Clinical Data Generation Federated Learning
Apheris AI GmbH · F. Hoffmann-La Roche Ltd.
Federated Learning for Early Prediction of EV Charging Demand
May 6, 2026Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis +2Electric Vehicle Charging Spatiotemporal Forecasting
Indigma Innovations
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
May 6, 2026Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7Federated Learning Aggregation Label Distribution Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · University of the Chinese Academy of Sciences, China +3
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
May 6, 2026Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2Incentive Mechanisms for Federated Learning Federated Learning
SIMIS Shanghai Shanghai, China · Tsinghua University Beijing, China · Keio University Yokohama, Japan +1
FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling
May 6, 2026Guangyi Zhang, Yi Dai, Yiyun He +1Federated Learning Heterogeneous Federated Learning
Department of Computer Science, University of California, Irvine · Department of Mathematics, University of California, San Diego
DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
May 5, 2026Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngole MboulaMulti-Source Domain Adaptation Domain Adaptation
Universit´e Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sigma Nova
Generalized Category Discovery in Federated Graph Learning
May 5, 2026Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +2Federated Graph Learning Federated Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China
A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
May 5, 2026Dario Filatrella, Ragnar Thobaben, Mikael SkoglundHierarchical Federated Learning Federated Learning
Department of Information Science and Engineering Royal Institute of Technology Stockholm, Sweden
Distributed Deep Variational Approach for Privacy-preserving Data Release
May 4, 2026Zahir Alsulaimawi, Huaping LiuInformation Bottleneck Privacy-Preserving ML
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
May 4, 2026Chamani Shiranthika, Hadi Hadizadeh, Parvaneh SaeediLatent Diffusion Models Split Federated Learning
School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

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Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis

Ozgu Goksu, Nicolas Pugeault

School of Computing Science University of Glasgow

Representation Collapse Non-IID Federated Learning Metric Learning Federated Learning

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Federated Learning (FL) enables decentralised model training across distributed clients without requiring data centralisation. However, the generalisation performance of the global model is usually degraded by data heterogeneity across clients, particularly under limited data availability and class imbalance. To address this challenge, we propose FedQuad, a novel method that explicitly enforces minimising intra-class representations while enabling inter-class splits across clients. By jointly minimising distances between positive pairs and maximising distances between negative pairs, the proposed approach mitigates representation misalignment introduced during model aggregation. We evaluate our method on CIFAR-10, CIFAR-100, and Tiny-ImageNet under diverse non-IID settings and varying numbers of clients, demonstrating consistent improvements over existing baselines. Additionally, we provide a comprehensive analysis of metric learning-based approaches in both centralised and federated environments, highlighting their effectiveness in alleviating representation collapse under heterogeneous data distributions.