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LiD-FL: Towards List-Decodable Federated Learning

arXiv 2408.04963 ↗First posted 9 Aug 2024cs.LGcited by 0

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Abstract (arXiv)

Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest workers, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Under proper assumptions on the loss function, we prove a convergence theorem for our method. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 9 Aug 2024.

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References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  9. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  10. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  11. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  12. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
20 more
  1. 2019Robust Aggregation for Federated Learningcited by 314
  2. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  3. 2019Can You Really Backdoor Federated Learning?cited by 250
  4. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  5. 2023Handbook of Convergence Theorems for (Stochastic) Gradient Methodscited by 158
  6. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  7. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 136
  8. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 125
  9. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  10. 2016Learning from Untrusted Datacited by 90
  11. 2017List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussianscited by 70
  12. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  13. 2020Byzantine-Resilient Non-Convex Stochastic Gradient Descentcited by 56
  14. 2021Approximate Byzantine Fault-Tolerance in Distributed Optimizationcited by 27
  15. 2023Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneitycited by 25
  16. 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learningcited by 24
  17. 2021On the Strategyproofness of the Geometric Mediancited by 9
  18. 2023High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackerscited by 7
  19. 2023Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outlierscited by 4
  20. 2024On the Relevance of Byzantine Robust Optimization Against Data Poisoningcited by 2

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