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Zeno++: Robust Fully Asynchronous SGD

arXiv 1903.07020 ↗First posted 17 Mar 2019cs.LG cs.DC stat.MLcited by 53

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Abstract (arXiv)

We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ removes some unrealistic restrictions on worker-server communications, allowing for fully asynchronous updates from anonymous workers, arbitrarily stale worker updates, and the possibility of an unbounded number of Byzantine workers. The key idea is to estimate the descent of the loss value after the candidate gradient is applied, where large descent values indicate that the update results in optimization progress. We prove the convergence of Zeno++ for non-convex problems under Byzantine failures. Experimental results show that Zeno++ outperforms existing approaches.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2019.

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Citing papers per year: 2021: 9; 2022: 10; 2023: 12; 2024: 7; 2025: 7; 2026: 8.

53 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  3. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  4. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 29
  5. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  6. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  7. 2023Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Surveycited by 18
  8. 2022Maximizing Communication Efficiency for Large-scale Training via 0/1 Adamcited by 15
  9. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  10. 2021Secure Byzantine-Robust Distributed Learning via Clusteringcited by 12
  11. 2022Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Datacited by 9
  12. 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 8
38 more
  1. 2023High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackerscited by 7
  2. 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackerscited by 6
  3. 2023Principled Federated Domain Adaptation: Gradient Projection and Auto-Weightingcited by 6
  4. 2022MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attackscited by 5
  5. 2022MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clusteringcited by 5
  6. 2021Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environmentscited by 4
  7. 2023ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitmentcited by 4
  8. 2023Can Membership Inferencing be Refuted?cited by 4
  9. 2024Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustnesscited by 4
  10. 2021Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learningcited by 3
  11. 2022Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimizationcited by 3
  12. 2023FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learningcited by 3
  13. 2023Online Learning with Adversaries: A Differential-Inclusion Analysiscited by 3
  14. 2023SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspectioncited by 3
  15. 2025Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participationcited by 3
  16. 2023Convergence of the Stochastic Heavy Ball Method With Approximate Gradients and/or Block Updatingcited by 2
  17. 2025Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous MLcited by 2
  18. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  19. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  20. 2021Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learningcited by 1
  21. 2021ARFED: Attack-Resistant Federated averaging based on outlier eliminationcited by 1
  22. 2022FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluderscited by 1
  23. 2023Can We Trust the Similarity Measurement in Federated Learning?cited by 1
  24. 2024Asynchronous Byzantine Federated Learningcited by 1
  25. 2024Understanding Byzantine Robustness in Federated Learning with A Black-box Servercited by 1
  26. 2025Orthogonal Calibration for Asynchronous Federated Learningcited by 1
  27. 2025H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learningcited by 1
  28. 2021FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learningcited by 0
  29. 2021MANDERA: Malicious Node Detection in Federated Learning via Rankingcited by 0
  30. 2022Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Resultscited by 0
  31. 2023Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Thingscited by 0
  32. 2024dSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGDcited by 0
  33. 2025Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor Itcited by 0
  34. 2026Enhancing Robustness of Federated Learning via Server Learningcited by 0
  35. 2026SecureAFL: Secure Asynchronous Federated Learningcited by 0
  36. 2026Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurementscited by 0
  37. 2026AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learningcited by 0
  38. 2026Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimatescited by 0

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