mlsanityData to 7 Oct 2026

FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure

arXiv 2010.06870 ↗First posted 14 Oct 2020cs.LG cs.DCcited by 45

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Abstract (arXiv)

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced (statistical heterogeneity) training data of FL is distributed in the federated network, which will increase the divergences between the local models and global model, further degrading performance. In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure. 3) implement a newcomer device cold start mechanism based on the auxiliary global model for framework scalability and practicality. FedGroup can achieve improvements by dividing joint optimization into groups of sub-optimization and can be combined with FL optimizer FedProx. The convergence and complexity are analyzed to demonstrate the efficiency of our proposed framework. We also evaluate FedGroup and FedGrouProx (combined with FedProx) on several open datasets and made comparisons with related CFL frameworks. The results show that FedGroup can significantly improve absolute test accuracy by +14.1% on FEMNIST compared to FedAvg. +3.4% on Sentiment140 compared to FedProx, +6.9% on MNIST compared to FeSEM.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 6; 2024: 13; 2025: 13; 2026: 9.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  3. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  4. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  5. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  6. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  7. 2024Spectral Co-Distillation for Personalized Federated Learningcited by 8
  8. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  9. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  10. 2024FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learningcited by 4
  11. 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learningcited by 4
  12. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
33 more
  1. 2022Federated Learning with Intermediate Representation Regularizationcited by 3
  2. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  3. 2024FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaborationcited by 2
  4. 2025FedPref: Federated Learning Across Heterogeneous Multi-objective Preferencescited by 2
  5. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  6. 2025Personalized Federated Learning via Learning Dynamic Graphscited by 2
  7. 2022Group Personalized Federated Learningcited by 1
  8. 2023Medical Federated Model with Mixture of Personalized and Sharing Componentscited by 1
  9. 2023REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settingscited by 1
  10. 2024Seamless Integration: Sampling Strategies in Federated Learning Systemscited by 1
  11. 2024Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteescited by 1
  12. 2025Federated Learning for Commercial Image Sourcescited by 1
  13. 2025Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learningcited by 1
  14. 2025Clustered Federated Learning with Hierarchical Knowledge Distillationcited by 1
  15. 2024DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learningcited by 0
  16. 2024Towards Client Driven Federated Learningcited by 0
  17. 2024Class-Wise Federated Averaging for Efficient Personalizationcited by 0
  18. 2024CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clusteringcited by 0
  19. 2024Heterogeneity: An Open Challenge for Federated On-board Machine Learningcited by 0
  20. 2024Comparative Evaluation of Clustered Federated Learning Methodscited by 0
  21. 2025One-Shot Clustering for Federated Learningcited by 0
  22. 2025One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumptioncited by 0
  23. 2025FedHK-MVFC: Federated Heat Kernel Multi-View Clusteringcited by 0
  24. 2025A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skewcited by 0
  25. 2025UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federationcited by 0
  26. 2026HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learningcited by 0
  27. 2026When To Adapt? Adapting the Model or Data in Federated Medical Imagingcited by 0
  28. 2026COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communicationcited by 0
  29. 2026Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challengescited by 0
  30. 2026Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detectioncited by 0
  31. 2026Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learningcited by 0
  32. 2026MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuningcited by 0
  33. 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Datacited by 0