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.

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Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
May 10, 2026Akihito Taya, Yuuki Nishiyama, Kaoru SezakiAlternating Direction Method of Multipliers Non-IID Federated Learning
Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan · Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
May 9, 2026Bingnan Xiao, Yuan Gao, Bingcong Li +3Sharpness-Aware Minimization Non-IID Federated Learning
Fudan University · ETH Zurich · Edith Cowan University +1
Evaluating Federated Learning approaches for mammography under breast density heterogeneity
May 9, 2026Gonzalo Iñaki Quintana, Franco Martin Di Maria, Laurence VancambergFederated Learning Aggregation Medical Image Analysis
GE HealthCare, 283 Rue de la Mini`ere, 78530 Buc, France · ENS Paris-Saclay, 4 Av. des Sciences, 91190 Gif-sur-Yvette, France · IMT Atlantique, 655 Av. du Technopˆole, 29280 Plouzan´e, France +1
MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift
May 9, 2026Kiran Naseer, Naveed Anwer ButtDistribution Shift Robustness BraTS
University of Gujrat, Pakistan
When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
May 9, 2026Kiran Naseer, Umar ShoaibNon-IID Federated Learning Federated Fine-Tuning
University of Gujrat, Pakistan
FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference
May 9, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Federated Learning Heterogeneous Federated Learning
Dept. of Electronic Engineering, BNRist, Tsinghua University. · Dept. of Electronic and Computer Engineering, HKUST.
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
May 9, 2026Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3LLM Evaluation LLM Fine-Tuning
Duke Kunshan University · Flower Labs · University of Cambridge
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
May 9, 2026Chia-Yuan Wu, Frank E. Curtis, Daniel P. RobinsonGroup Fairness Federated Learning
Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA
Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
May 8, 2026Guangchen LanFederated RL Privacy-Preserving Language Models
Elmore Family School of Electrical and Computer Engineering, West Lafayette, Indiana · THE PURDUE UNIVERSITY GRADUATE SCHOOL
Private Vertical Federated Inference for Time-Series
May 8, 2026Lucas Fenaux, Larris Xie, Aditya Bang +3Efficient Neural Network Inference Privacy-Preserving ML
University of Waterloo · Borealis AI
FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy
May 8, 2026Debashis De, Mahua Nandy Pal, Dipankar HazraDiabetic Retinopathy Grading Quantum Machine Learning
Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal · Department of Computer Science and Engineering, MCKV Institute of Engineering, West Bengal · Department of Computer Science and Engineering, OmDayal Group of Institutions, West Bengal
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning
May 8, 2026Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2LLM Fine-Tuning Language Model Self-Improvement
Purdue University · Yonsei University · University at Buffalo-SUNY
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
May 8, 2026Fabian Stricker, Jose A. Peregrina, David Bermbach +1Federated Learning Aggregation Federated Learning
Institute of Data-Centric Software Systems (IDSS), Hochschule Karlsruhe, Karlsruhe, Germany · Scalable Software Systems Research Group, TU Berlin, Berlin, Germany
Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
May 8, 2026Hanlin Cai, Kai Li, Houtianfu Wang +4LLM Fine-Tuning LLM Security
Centre for neXt Communications (CXC), Department of Engineering, University of Cambridge, CB3 0FA Cambridge, U.K. · Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855, Luxembourg · Telecommunication Networks group (TKN) at the School of Electrical Engineering and Computer Science, TU Berlin, Germany +1
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 Cross-Client Subgraph 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

May 10, 2026, cs.LGJ/K move · Enter open · S save

Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective

Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki

Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan · Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan

Alternating Direction Method of Multipliers Non-IID Federated Learning Federated Learning

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Decentralized federated learning (FL) is a promising approach for training machine learning models on sensor networks, Internet of Things (IoT) devices, and other edge systems where no central server exists. While federated learning offers advantages such as preserving data privacy, it often suffers from non-independent and identically distributed (IID) data distributions across devices, which cause significant performance degradation. This issue is particularly severe when directly optimizing model parameters, because neural network training is inherently non-convex and standard convergence guarantees for convex optimization do not apply. Unlike existing decentralized FL methods that primarily operate in parameter space, we propose federated function-space alternating direction method of multipliers (FedF-ADMM). FedF-ADMM exploits the convexity of loss functionals within function space to derive alternating direction method of multipliers (ADMM)-based update directions, which are subsequently projected onto the parameter space via knowledge distillation. We further introduce a stabilization coefficient to enhance robustness under severe non-IID settings and analyze its behavior from a control-theoretic perspective by interpreting it as a proportional-integral (PI) term. Experiments under challenging non-IID scenarios, including settings where each device has data from only a single label, demonstrate that FedF-ADMM achieves faster and more stable convergence than existing decentralized FL methods, while attaining higher accuracy and better consensus among devices.

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