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Robust Federated Learning in a Heterogeneous Environment

arXiv 1906.06629 ↗First posted 16 Jun 2019cs.LG stat.MLcited by 88

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Abstract (arXiv)

We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computational challenges arise in Federated Learning particularly in the presence of heterogeneous data distribution (i.e., data points on different devices belong to different distributions signifying different clusters) and Byzantine machines (i.e., machines that may behave abnormally, or even exhibit arbitrary and potentially adversarial behavior). To address the aforementioned challenges, first we propose a general statistical model for this problem which takes both the cluster structure of the users and the Byzantine machines into account. Then, leveraging the statistical model, we solve the robust heterogeneous Federated Learning problem optimally; in particular our algorithm matches the lower bound on the estimation error in dimension and the number of data points. Furthermore, as a by-product, we prove statistical guarantees for an outlier-robust clustering algorithm, which can be considered as the Lloyd algorithm with robust estimation. Finally, we show via synthetic as well as real data experiments that the estimation error obtained by our proposed algorithm is significantly better than the non-Byzantine-robust algorithms; in particular, we gain at least by 53% and 33% for synthetic and real data experiments, respectively, in typical settings.

Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 15; 2023: 19; 2024: 19; 2025: 11; 2026: 5.

88 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2021A Field Guide to Federated Optimizationcited by 312
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  4. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  5. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  6. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  7. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  8. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  9. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  10. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  11. 2021Adversarial Attacks On Multi-Agent Communicationcited by 48
  12. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
38 more
  1. 2022Federated Learning under Distributed Concept Driftcited by 35
  2. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  3. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  4. 2021Exploiting Heterogeneity in Robust Federated Best-Arm Identificationcited by 20
  5. 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
  6. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  7. 2023A Huber Loss Minimization Approach to Byzantine Robust Federated Learningcited by 16
  8. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  9. 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splittingcited by 13
  10. 2021Secure Byzantine-Robust Distributed Learning via Clusteringcited by 12
  11. 2022An Optimal Transport Approach to Personalized Federated Learningcited by 12
  12. 2023A Survey on Class Imbalance in Federated Learningcited by 12
  13. 2021Communication-Efficient and Personalized Federated Lottery Ticket Learningcited by 10
  14. 2021Dynamic Attention-based Communication-Efficient Federated Learningcited by 9
  15. 2022Decentralized Low-Latency Collaborative Inference via Ensembles on the Edgecited by 9
  16. 2024Co-clustering for Federated Recommender Systemcited by 9
  17. 2023Differentially Private Federated Clustering over Non-IID Datacited by 7
  18. 2023Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networkscited by 7
  19. 2022Global Convergence of Federated Learning for Mixed Regressioncited by 6
  20. 2022BOBA: Byzantine-Robust Federated Learning with Label Skewnesscited by 6
  21. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  22. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  23. 2021LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learningcited by 4
  24. 2021FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learningcited by 4
  25. 2022FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Datacited by 4
  26. 2022Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Boundscited by 4
  27. 2023Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outlierscited by 4
  28. 2024Position: Cracking the Code of Cascading Disparity Towards Marginalized Communitiescited by 4
  29. 2022A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environmentscited by 3
  30. 2022Federated Learning with Intermediate Representation Regularizationcited by 3
  31. 2024BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learningcited by 3
  32. 2024AI-Aided Kalman Filterscited by 3
  33. 2025FedEAT: A Robustness Optimization Framework for Federated LLMscited by 3
  34. 2021Aggregation Delayed Federated Learningcited by 2
  35. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  36. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  37. 2025AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learningcited by 2
  38. 2021An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learningcited by 1

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