mlsanityData to 7 Oct 2026

Federated Learning Clients Clustering with Adaptation to Data Drifts

arXiv 2411.01580 ↗First posted 3 Nov 2024cs.LG cs.CRcited by 3

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Abstract (arXiv)

Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limits global model accuracy. Clustered FL (CFL) mitigates this by grouping clients with similar representations and training a separate model for each cluster. In practice, client data evolves over time, a phenomenon we refer to as data drift, which breaks cluster homogeneity and degrades performance. Data drift can take different forms depending on whether changes occur in the output values, the input features, or the relationship between them. We propose FIELDING, a CFL framework for handling diverse types of data drift with low overhead. FIELDING detects drift at individual clients and performs selective re-clustering to balance cluster quality and model performance, while remaining robust to malicious clients and varying levels of heterogeneity. Experiments show that FIELDING improves final model accuracy by 1.9-5.9% and achieves target accuracy 1.16x-2.23x faster than existing state-of-the-art CFL methods.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 39 of its 80 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

39 of 80 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  7. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  8. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  9. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  10. 2018Federated Learning with Non-IID Datacited by 1,099
  11. 2020Adaptive Federated Optimizationcited by 1,036
  12. 2019Towards Federated Learning at Scale: System Designcited by 826
27 more
  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  5. 2019Fair Resource Allocation in Federated Learningcited by 389
  6. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 364
  7. 2017Functional Map of the Worldcited by 338
  8. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  9. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  10. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  11. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  12. 2020A Unified View of Label Shift Estimationcited by 119
  13. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  14. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  15. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  16. 2020Ekya: Continuous Learning of Video Analytics Models on Edge Compute Serverscited by 58
  17. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  18. 2022Federated Learning under Distributed Concept Driftcited by 35
  19. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  20. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  21. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  22. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  23. 2019Locally Private k-Means Clusteringcited by 15
  24. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 9
  25. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  26. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  27. 2024HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learningcited by 0
  2. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0
  3. 2026FedTVD: Balancing Data Quality and Quantity for Robust Federated Learningcited by 0