mlsanityData to 7 Oct 2026

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

arXiv 1803.01498 ↗First posted 5 Mar 2018cs.LG cs.CR cs.DCcited by 698

Not on the map. Why?

Cited by 40 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.

Key facts. Cited by 698 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 68; 2022: 99; 2023: 113; 2024: 139; 2025: 153; 2026: 126.

698 papers.

Sort:

By area:

  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  3. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  4. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 97
  5. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  6. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  7. 2023Multimodal Federated Learning via Contrastive Representation Ensemblecited by 85
  8. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  9. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 78
  10. 2022MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clientscited by 65
  11. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 64
  12. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
38 more
  1. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  2. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
  3. 2022FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Informationcited by 53
  4. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 52
  5. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  6. 2024Byzantine-Robust Decentralized Federated Learningcited by 47
  7. 2021SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsificationcited by 46
  8. 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 46
  9. 2022VeriFi: Towards Verifiable Federated Unlearningcited by 41
  10. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  11. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  12. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  13. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  14. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
  15. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 33
  16. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  17. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  18. 2023An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learningcited by 32
  19. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  20. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 29
  21. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 29
  22. 2022FLCert: Provably Secure Federated Learning against Poisoning Attackscited by 29
  23. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  24. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  25. 2023Integration of Large Language Models and Federated Learningcited by 28
  26. 2023On the Vulnerability of Backdoor Defenses for Federated Learningcited by 27
  27. 2023A Survey on Decentralized Federated Learningcited by 27
  28. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
  29. 2022A Survey on Federated Recommendation Systemscited by 25
  30. 2023Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneitycited by 25
  31. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learningcited by 25
  32. 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learningcited by 24
  33. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  34. 2021RoFL: Robustness of Secure Federated Learningcited by 23
  35. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  36. 2023BayBFed: Bayesian Backdoor Defense for Federated Learningcited by 23
  37. 2023Multi-metrics adaptively identifies backdoors in Federated learningcited by 23
  38. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22

50 most cited of 698 All 698 (JSON)