Byzantine-Robust Federated Learning

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Overview

Byzantine-robust federated learning studies how to train a shared model when some participating clients may be faulty or malicious, without centralizing their data. Common defenses replace simple averaging with coordinate medians or trimmed means, Krum-style distance selection, geometric-median or clustering methods, and trust, anomaly, or history-based screening; newer systems also combine these with secure aggregation, differential privacy, or proofs of correct computation. Methods are tested on non-IID image and text benchmarks, intrusion detection and medical data, and increasingly decentralized, asynchronous, or edge settings, where partial participation and client diversity complicate detection. The central tension is separating malicious updates from legitimate variation while retaining accuracy and convergence: adaptive or coordinated attacks can evade filters, and guarantees often depend on assumptions about attacker numbers, honest-client structure, or available reference data.

Latest papers

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84 papers15 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

  1. FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

    Apr 16, 2026He Yang, Dongyi Lv, Wei Xi +3Federated Learning AggregationByzantine-Robust Federated Learning

  2. XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers

    Apr 10, 2026Israt Jahan Mouri, Muhammad Ridowan, Muhammad Abdullah AdnanFederated LearningByzantine-Robust Federated Learning

    Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh · TigerIT Bangladesh Ltd., Dhaka, Bangladesh

  3. SecureAFL: Secure Asynchronous Federated Learning

    Apr 4, 2026Anjun Gao, Feng Wang, Zhenglin Wan +3Federated LearningAsynchronous Federated Learning

    University of Louisville, Louisville, USA · Northeastern University, Shenyang, China · National University of Singapore, Singapore, Singapore +1

  4. Enhancing Robustness of Federated Learning via Server Learning

    Apr 3, 2026Van Sy Mai, Kushal Chakrabarti, Richard J. La +1Federated LearningByzantine-Robust Federated Learning

    National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA · Tata Consultancy Services Research, Mumbai, Maharashtra 400607, India · the University of Maryland, College Park, MD 20742, USA +1

  5. FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation

    Mar 30, 2026Ruiyang Wang, Rong Pan, Zhengan YaoPrivacy-Preserving Federated LearningFederated Learning

    School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China · School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China · Institute of Advanced Studies Hong Kong, Sun Yat-sen University, Hong Kong

  6. Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

    Mar 26, 2026Paul Shepherd, Tasos Dagiuklas, Bugra Alkan +1Trust-Aware Federated LearningIndustrial Internet of Things

    London South Bank University, London, UK. · Instituto de Telecomunica¸c˜oes, Aveiro, Portugal.

  7. Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions

    Mar 24, 2026Rustem Islamov, Grigory Malinovsky, Alexander Gaponov +3Byzantine-Robust Federated LearningDifferentially Private Federated Learning

    University of Basel, Switzerland · KAUST, Saudi Arabia · MBZUAI, UAE

  8. FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy

    Mar 19, 2026Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clément Larose +3Trust-Aware Federated LearningFederated Learning

    Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Departamento de Ciencias Computacionales, Guadalajara, 45017, Jalisco, Mexico · Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey, 64849, N.L., Mexico · CRAN UMR 7039, Université de Lorraine and CNRS, Vandœuvre-lès-Nancy, France +1

  9. Dynamic Meta-Layer Aggregation for Byzantine-Robust Federated Learning

    Mar 17, 2026Reek Das, Biplab Kanti SenByzantine-Robust Federated LearningFederated Learning Aggregation

    APC Roy Government College · Assistant Professor, Department of Computer Science P.R. Thakur Government College

  10. Byzantine-Robust Optimization under (L0,L1)(L_0, L_1)-Smoothness

    Mar 12, 2026Arman Bolatov, Samuel Horváth, Martin Takáč +1Distributed OptimizationByzantine-Robust Federated Learning

    Mohamed bin Zayed University of Artificial Intelligence, UAE

  11. Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case

    Mar 12, 2026Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +2Byzantine-Robust Federated LearningEdge-Cloud Computing

    atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain

  12. Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI

    Mar 3, 2026Edouard LansiauxQuantum Federated LearningPrivacy-Preserving Federated Learning

    Department of Emergency Medicine & STaR-AI Research Group, CHU de Lille, Université de Lille, Lille, France

  13. Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health

    Feb 27, 2026Paul Shepherd, Tasos Dagiuklas, Bugra Alkan +2Trust-Aware Federated LearningFederated Learning

    London South Bank University, London, UK · Instituto de Telecomunica¸c˜oes, Aveiro, Portugal

  14. CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning

    Feb 25, 2026Arnab Nath, Harsh KasyapFederated Learning AggregationByzantine-Robust Federated Learning

    Indian Institute of Technology (BHU), Varanasi, India

  15. A Secure and Private Distributed Bayesian Federated Learning Design

    Feb 23, 2026Nuocheng Yang, Sihua Wang, Zhaohui Yang +3Federated LearningDecentralized Learning

    Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China, and Zhejiang Provincial Key Lab of Information Processing, Communication and Networking (IPCAN), Hangzhou 310007, China · Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami, Coral Gables, FL, 33146 USA +1

  16. Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing

    Feb 20, 2026Ehsan Lari, Reza Arablouei, Stefan WernerFederated Conformal PredictionFederated Learning Aggregation

    Department of Electronic Systems, Norwegian University of Science and Technology, 7491 Trondheim, Norway · Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia · Department of Information and Communications Engineering, Aalto University, 00076, Finland

  17. PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT

    Feb 20, 2026Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan +3Blockchain-Based Federated LearningIoT Intrusion Detection

    Information Security Lab, University of Information Technology, Ho Chi Minh City, Vietnam. · Vietnam National University, Ho Chi Minh City, Vietnam. · VNU-HCM Information Security Center, Ho Chi Minh City, Vietnam.

  18. A Byzantine-Robust Federated Learning with Nesterov Momentum

    Nov 4, 2025Lihan Xu, Xiaoyi Fan, Gang Wang +3Federated Learning AggregationByzantine-Robust Federated Learning

    School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China. · the Artificial Intelligence Research Institute and Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen 518172, China. · the State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.

  19. Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning

    Jun 22, 2025Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Byzantine-Robust Federated LearningAlgorithmic Stability

    PreMeDICaL team, Inria, Idesp, Inserm, Université de Montpellier, Montpellier, France · Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189, CRIStAL, F-59000 Lille, France · Department of Computer Science, University of Copenhagen, Copenhagen, Danemark

  20. Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation

    May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning AggregationIndependent Component Analysis

    University of Toronto · National University of Singapore · Rutgers University

  21. Byzantine-Robust Aggregation for Securing Decentralized Federated Learning

    Sep 26, 2024Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +1Federated Learning AggregationByzantine-Robust Federated Learning

    atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain

  22. Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

    Apr 30, 2024Yi Li, Renyou Xie, Chaojie Li +2Byzantine-Robust Federated LearningElectric Vehicle Charging

    College of Electronic and Information Engineering, Southwest University · Department of Electrical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong

  23. FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge

    Feb 26, 2024Tianhang Zheng, Yanlu Li, Bohan Deng +1Federated Learning AggregationByzantine-Robust Federated Learning

    University of Missouri-Kansas City · University of Toronto

  24. Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification

    Oct 6, 2023Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4Byzantine-Robust Federated LearningAdversarial Attacks

    University of California, Irvine · Texas A&M University · University of Birmingham +3