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Generalized Byzantine-tolerant SGD

arXiv 1802.10116 ↗First posted 27 Feb 2018cs.DC stat.MLcited by 94

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Abstract (arXiv)

We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server~(PS) architecture. We prove the Byzantine resilience properties of these aggregation rules. Empirical analysis shows that the proposed techniques outperform current approaches for realistic use cases and Byzantine attack scenarios.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 14; 2023: 22; 2024: 22; 2025: 15; 2026: 2.

94 papers.

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  1. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  2. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 64
  3. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  4. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  5. 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 46
  6. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 29
  7. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 29
  8. 2023A Survey on Decentralized Federated Learningcited by 27
  9. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  10. 2021Exploiting Heterogeneity in Robust Federated Best-Arm Identificationcited by 20
  11. 2021Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?cited by 17
  12. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
38 more
  1. 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updatescited by 13
  2. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  3. 2023FedDefender: Client-Side Attack-Tolerant Federated Learningcited by 12
  4. 2021Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inferencecited by 11
  5. 2024MedBN: Robust Test-Time Adaptation against Malicious Test Samplescited by 10
  6. 2023Client-specific Property Inference against Secure Aggregation in Federated Learningcited by 9
  7. 2023Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learningcited by 9
  8. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  9. 2022Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learningcited by 8
  10. 2023Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environmentcited by 8
  11. 2023Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacycited by 8
  12. 2025Towards Trustworthy Federated Learning with Untrusted Participantscited by 8
  13. 2021Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancycited by 7
  14. 2022Byzantine-Robust Federated Linear Banditscited by 7
  15. 2022Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learningcited by 7
  16. 2023Towards Attack-tolerant Federated Learning via Critical Parameter Analysiscited by 7
  17. 2024Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learningcited by 7
  18. 2021Certified Federated Adversarial Trainingcited by 6
  19. 2022zPROBE: Zero Peek Robustness Checks for Federated Learningcited by 6
  20. 2023A Comprehensive Survey On Client Selections in Federated Learningcited by 6
  21. 2024Global Convergence Guarantees for Federated Policy Gradient Methods with Adversariescited by 6
  22. 2021SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregationcited by 5
  23. 2021Robust Federated Learning via Over-The-Air Computationcited by 5
  24. 2022Security Analysis of SplitFed Learningcited by 5
  25. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  26. 2021LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learningcited by 4
  27. 2021Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environmentscited by 4
  28. 2022Over-The-Air Federated Learning under Byzantine Attackscited by 4
  29. 2022Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Boundscited by 4
  30. 2022Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithmscited by 4
  31. 2023Temporal Gradient Inversion Attacks with Robust Optimizationcited by 4
  32. 2023Client-side Gradient Inversion Against Federated Learning from Poisoningcited by 4
  33. 2024Federated Learning Resilient to Byzantine Attacks and Data Heterogeneitycited by 4
  34. 2025Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studycited by 4
  35. 2021Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learningcited by 3
  36. 2021Combining Differential Privacy and Byzantine Resilience in Distributed SGDcited by 3
  37. 2022A simplified convergence theory for Byzantine resilient stochastic gradient descentcited by 3
  38. 2023Mitigating Backdoors in Federated Learning with FLDcited by 3

50 most cited of 94 All 94 (JSON)