mlsanityData to 14 Oct 2026

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering

arXiv 2301.12379 ↗First posted 29 Jan 2023cs.LGcited by 15

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Abstract (arXiv)

Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning system. Though recent research has focused on improving the optimization of FL when distribution shifts occur among clients, ensuring global performance when multiple types of distribution shifts occur simultaneously among clients -- such as feature distribution shift, label distribution shift, and concept shift -- remain under-explored. In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges. Through our research, we find that existing methods fail to address the clustering principle. Therefore, we propose a novel clustering algorithm framework, dubbed as FedRC, which adheres to our proposed clustering principle by incorporating a bi-level optimization problem and a novel objective function. Extensive experiments demonstrate that FedRC significantly outperforms other SOTA cluster-based FL methods. Our code is available at urlhttps://github.com/LINs-lab/FedRC.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 42 of its 64 references that are in the map. First posted on arXiv 29 Jan 2023.

Builds on

42 of 64 references.

References · 42

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  9. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  10. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  11. 2021Model-Contrastive Federated Learningcited by 766
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 618
30 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  4. 2020Learning under Concept Drift: A Reviewcited by 477
  5. 2019Agnostic Federated Learningcited by 456
  6. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  7. 2020Adaptive Personalized Federated Learningcited by 358
  8. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  9. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  10. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  11. 2018On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimizationcited by 174
  12. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  13. 2019Federated Adversarial Domain Adaptationcited by 140
  14. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  15. 2021Distributionally Robust Federated Averagingcited by 104
  16. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  17. 2020On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problemscited by 80
  18. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  19. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  20. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 46
  21. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  22. 2022Federated Learning under Distributed Concept Driftcited by 36
  23. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  24. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 30
  25. 2022Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)cited by 27
  26. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  27. 2021FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptationcited by 9
  28. 2021FRuDA: Framework for Distributed Adversarial Domain Adaptationcited by 8
  29. 2020An Adaptive EM Accelerator for Unsupervised Learning of Gaussian Mixture Modelscited by 3
  30. 2022Quantifying the Impact of Label Noise on Federated Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 8; 2026: 1.

15 papers.

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  1. 2025FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Modelscited by 11
  2. 2024FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantificationcited by 4
  3. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  4. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  5. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
  6. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  7. 2024FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocationcited by 1
  8. 2025Distilling A Universal Expert from Clustered Federated Learningcited by 1
  9. 2025FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learningcited by 1
  10. 2024Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexingcited by 0
  11. 2025Clustered Federated Learning via Embedding Distributionscited by 0
  12. 2025HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregationcited by 0
3 more
  1. 2025IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimationcited by 0
  2. 2025A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learningcited by 0
  3. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0