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Byzantine-Robust Clustered Federated Learning

arXiv 2306.00638 ↗First posted 1 Jun 2023stat.ML cs.DC cs.LGcited by 0

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Abstract (arXiv)

This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clusters. In this setting, non-Byzantine machines in the same cluster have the same underlying data distribution, and different clusters of non-Byzantine machines have different learning tasks. Byzantine machines can adversarially attack any cluster and disturb the training process on clusters they attack. In the presence of Byzantine machines, the goal of our work is to identify cluster membership of non-Byzantine machines and optimize the models learned by each cluster. We adopt the Iterative Federated Clustering Algorithm (IFCA) framework of Ghosh et al. (2020) to alternatively estimate cluster membership and optimize models. In order to make this framework robust against adversarial attacks from Byzantine machines, we use coordinate-wise trimmed mean and coordinate-wise median aggregation methods used by Yin et al. (2018). Specifically, we propose a new Byzantine-Robust Iterative Federated Clustering Algorithm to improve on the results in Ghosh et al. (2019). We prove a convergence rate for this algorithm for strongly convex loss functions. We compare our convergence rate with the convergence rate of an existing algorithm, and we demonstrate the performance of our algorithm on simulated data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 1 Jun 2023.

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References · 23

  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. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2019Agnostic Federated Learningcited by 451
  10. 2019Robust Aggregation for Federated Learningcited by 314
  11. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  12. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
11 more
  1. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  2. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  3. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  4. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  5. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  6. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  7. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  8. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  9. 2019Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attackscited by 79
  10. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  11. 2017Byzantine-Tolerant Machine Learningcited by 18

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