mlsanityData to 7 Oct 2026

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

arXiv 2112.06053 ↗First posted 11 Dec 2021cs.LGcited by 46

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Abstract (arXiv)

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 11 Dec 2021.

Builds on

18 of 25 references.

References · 18

  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  11. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  12. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
6 more
  1. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  2. 2019One-Shot Federated Learningcited by 124
  3. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  4. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  5. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  6. 2019An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Modelscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 10; 2024: 14; 2025: 13; 2026: 4.

46 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  3. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  4. 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
  5. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  6. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  7. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  8. 2024Co-clustering for Federated Recommender Systemcited by 9
  9. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  10. 2023FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimizationcited by 7
  11. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  12. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
34 more
  1. 2024Differentially Private Clustered Federated Learningcited by 4
  2. 2024FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learningcited by 4
  3. 2022Combating Client Dropout in Federated Learning via Friend Model Substitutioncited by 3
  4. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  5. 2024Fair Concurrent Training of Multiple Models in Federated Learningcited by 3
  6. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  7. 2022An Improved Algorithm for Clustered Federated Learningcited by 2
  8. 2023Federated Two Stage Decoupling With Adaptive Personalization Layerscited by 2
  9. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  10. 2025Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraftcited by 2
  11. 2023REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settingscited by 1
  12. 2024Federated Cross-Training Learners for Robust Generalization under Data Heterogeneitycited by 1
  13. 2024TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clusteringcited by 1
  14. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  15. 2025MMiC: Mitigating Modality Incompleteness in Clustered Federated Learningcited by 1
  16. 2025Distilling A Universal Expert from Clustered Federated Learningcited by 1
  17. 2025FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learningcited by 1
  18. 2025Operator-Theoretic Framework for Gradient-Free Federated Learningcited by 1
  19. 2023IP-FL: Incentivized and Personalized Federated Learningcited by 0
  20. 2024DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learningcited by 0
  21. 2024Towards Client Driven Federated Learningcited by 0
  22. 2024Novel clustered federated learning based on local losscited by 0
  23. 2024Comparative Evaluation of Clustered Federated Learning Methodscited by 0
  24. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0
  25. 2025One-Shot Clustering for Federated Learningcited by 0
  26. 2025Clustered Federated Learning via Embedding Distributionscited by 0
  27. 2025One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumptioncited by 0
  28. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0
  29. 2025SGFusion: Stochastic Geographic Gradient Fusion in Federated Learningcited by 0
  30. 2025Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learningcited by 0
  31. 2026Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendationcited by 0
  32. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0
  33. 2026Few-for-Many Personalized Federated Learningcited by 0
  34. 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inferencecited by 0