Federated Learning

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Overview

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

CardsList

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

  1. Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information

    May 4, 2026Sumedh J. Dongare, Patrick Weber, Andrea Ortiz +3Federated RLDecentralized MARL

    Communications Engineering Lab at the Technical University of Darmstadt, Germany · Chair of Information Systems and Information Management at the Goethe University Frankfurt, Germany · Institute of Telecommunications at the Vienna University of Technology, Austria

  2. Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

    May 4, 2026Judith Sáinz-Pardo Díaz, Álvaro López GarcíaPrivacy-Preserving MLDifferentially Private Federated Learning

  3. Graph Federated Unlearning for Privacy Preservation

    May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated UnlearningFederated Graph Learning

    Shanghai Jiao Tong University · Nanjing University of Science and Technology · Hong Kong Baptist University China

  4. Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning

    May 4, 2026Shihao Hou, Chikai Shang, Zhiheng Yang +5Class-Imbalanced LearningFine-Tuning

    Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China · Department of Statistical Science, University College London, London, United Kingdom · School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

  5. Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures

    May 4, 2026Divya GuptaByzantine-Robust Federated LearningPrivacy-Preserving ML

    Independent Researcher Hyderabad, India

  6. Personalized Federated Learning for Gradient Alignment

    May 4, 2026Dongwon Kim, Gyuejeong LeeFederated Learning AggregationPAC-Bayesian Theory

    SAKAK Inc., Korea · Div. of Artificial Intelligence and Data Science, Korea Cyber University, Korea

  7. FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training

    May 4, 2026Yijiang Li, Emon Dey, Zilinghan Li +3Federated Learning AggregationHigh-Performance Computing

    Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA · Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA

  8. Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery

    May 4, 2026Wenwei Zhao, Xiaowen Li, Yao Liu +1Federated UnlearningMachine Unlearning

    Department of Electrical Engineering, University of South Florida,Tampa, FL, USA · Bellini College, University of South Florida, Tampa, FL, USA

  9. Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction

    May 3, 2026G. Victor DanielGraph Neural NetworksPrivacy-Preserving ML

    School of Engineering, Anurag University, Hyderabad, Telangana, India

  10. Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection

    May 3, 2026Samhita Kuili, Mohammadreza Amini, Burak KantarciWireless CommunicationsPrivacy-Preserving ML

    School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

  11. EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

    May 1, 2026Zihao Ding, Beining Wu, Jun HuangCross-Modal LearningMultimodal Federated Learning

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

  12. FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization

    May 1, 2026Zoe Fowler, Ghassan AlRegibFederated Learning AggregationCross-Domain Generalization

    Georgia Institute of Technology

  13. Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration

    May 1, 2026Luru Jing, Cong Cong, Yanyuan Chen +1Computational PathologyDataset Distillation

    School of Computer Science, Peking University, Beijing, China · Center for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia · School of Data Science, University of Virginia, Charlottesville, VA, USA.

  14. Scaling Federated Linear Contextual Bandits via Sketching

    May 1, 2026Hantao Yang, Hong Xie, Xutong Liu +1Communication-Efficient Distributed TrainingContextual Bandits

    University of Science and Technology of China · University of Washington Tacoma

  15. Federated Learning with Hypergradient-based Online Update of Aggregation Weights

    May 1, 2026Ayano Nakai-Kasai, Tadashi WadayamaFederated Learning AggregationHypergradient Optimization

    Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

  16. Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design

    May 1, 2026Seyed Mohammad Azimi-Abarghouyi, Mehdi Bennis, Leandros TassiulasHierarchical Federated LearningCommunication-Efficient Distributed Training

    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden · Centre for Wireless Communications, University of Oulu, Oulu, Finland · Department of Electrical Engineering, Yale University, New Haven, USA

  17. A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

    Apr 30, 2026Zawad Yalmie Sazid, Robert Abbas, Sasa MaricIntelligent Transportation SystemsNetwork Intrusion Detection

    Victoria University, Sydney, Australia · University of New South Wales College, Sydney, Australia

  18. FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

    Apr 30, 2026Zhiqiang Kou, Junxiang Wu, Wenke Huang +8Multi-Label ClassificationFederated 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 · Wuhan University, China +3

  19. Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning

    Apr 30, 2026Yuhua Wang, Qinnan Zhang, Xiaodong Li +6Privacy-Preserving MLDifferentially Private Federated Learning

    School of Artificial Intelligence, Beihang University · School of Statistics, Renmin University of China · School of Computer Science and Engineering, Beihang University

  20. Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

    Apr 30, 2026Gaurang Sharma, Juha Pajula, Aada Illikainen +4Homomorphic EncryptionPrivacy-Preserving ML

    VTT Technical Research Centre of Finland Ltd, 33720, Tampere, Finland · VTT Technical Research Centre of Finland Ltd, 02150, Espoo, Finland · Solita Oy, 00100 Helsinki, Finland +1

  21. FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning

    Apr 30, 2026Mahad Ali, Laura J. BrattainNon-IID Federated LearningClustered Federated Learning

    University of Central Florida, Orlando FL 32816, USA

  22. AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning

    Apr 30, 2026Zehui Tang, Yuchen Liu, Feihu HuangFederated Learning AggregationByzantine-Robust Federated Learning

    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China · MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.

  23. Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

    Apr 29, 2026Zhaoyuan Cai, Xinglin ZhangFederated UnlearningMedical Imaging

    School of Computer Science and Engineering South China University of Technology Guangzhou, China

  24. Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases

    Apr 29, 2026Gota MorishitaInverse Probability WeightingFederated Learning Aggregation

    Centre for Brain, Mind and Markets University of Melbourne Parkville, Victoria, Australia

  25. SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning

    Apr 29, 2026Yimeng Shan, Zhaorui Zhang, Sheng Di +3Communication-Efficient Distributed TrainingLLM Fine-Tuning

    Hong Kong Polytechnic University Hong Kong · Argonne National Labratory USA · University of California, Merced USA +1

  26. CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

    Apr 29, 2026Fengchun Zhang, Qiang Ma, Liuyu Xiang +3Cross-Domain GeneralizationFederated Domain Generalization

    School of Information and Software Engineering, University of Electronic Science and Technology of China · 2QiYuan Lab · School of Artificial Intelligence, Beijing University of Posts and Telecommunications +1

  27. Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation

    Apr 29, 2026Martina Pavan, Matteo Caligiuri, Francesco Barbato +1Class-Imbalanced LearningSynthetic Data Augmentation

    University of Padova, Padova (PD), Italy · NIDEK Technologies Srl, Albignasego (PD), Italy

  28. Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

    Apr 28, 2026Teetat Pipattaratonchai, Aueaphum AueawatthanaphisutPrivacy-Preserving MLFederated Learning

    Science Math Triamudom Suksa Bangkok, Thailand · School of Information, Computer, and Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand

  29. Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification

    Apr 28, 2026Athanasios Papanikolaou, Athanasios Tziouvaras, Pavlos Stoikos +6Hierarchical Federated LearningPlant Disease Classification

    University of Zagreb, Croatia · University of Thessaly, Greece · University of Glasgow, UK +2

  30. Subspace Optimization for Efficient Federated Learning under Heterogeneous Data

    Apr 28, 2026Shuchen Zhu, Zhengyang Huang, Yuqi Xu +1Subspace OptimizationNon-IID Federated Learning

    Peking University · Beihang University