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Multi-Center Federated Learning: Clients Clustering for Better Personalization

arXiv 2108.08647 ↗First posted 19 Aug 2021cs.LGcited by 112

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Abstract (arXiv)

Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision-making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models' parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository https://github.com/mingxuts/multi-center-fed-learning .

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). Builds on 45 of its 87 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

45 of 87 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2018Federated Learning with Non-IID Datacited by 1,099
  8. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
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  11. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  12. 2020The Future of Digital Health with Federated Learningcited by 625
33 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  2. 2020Federated Learning with Matched Averagingcited by 564
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  4. 2019Federated Learning with Personalization Layerscited by 533
  5. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  6. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  7. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  9. 2019Federated Machine Learning: Concept and Applicationscited by 409
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  11. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  12. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  13. 2020Adaptive Personalized Federated Learningcited by 353
  14. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  15. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  16. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  17. 2017EMNIST: an extension of MNIST to handwritten letterscited by 212
  18. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  19. 2020Threats to Federated Learning: A Surveycited by 171
  20. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  21. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  22. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  23. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
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  26. 2021Dynamic Network Embedding Surveycited by 44
  27. 2019Real-World Image Datasets for Federated Learningcited by 30
  28. 2020Heterogeneous Federated Learningcited by 19
  29. 2020Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Datacited by 9
  30. 2020Self-Attention Enhanced Patient Journey Understanding in Healthcare Systemcited by 7
  31. 2019Revocable Federated Learning: A Benchmark of Federated Forestcited by 6
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 15; 2023: 30; 2024: 26; 2025: 24; 2026: 11.

112 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  3. 2021Federated Learning for Open Bankingcited by 138
  4. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  7. 2023Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Datacited by 40
  8. 2022Personalized Federated Learning With Graphcited by 29
  9. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  10. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  11. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  12. 2023MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligencecited by 20
38 more
  1. 2023Dual Personalization on Federated Recommendationcited by 19
  2. 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
  3. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  4. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  5. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  6. 2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
  7. 2022An Optimal Transport Approach to Personalized Federated Learningcited by 12
  8. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  9. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  10. 2023Federated Recommendation with Additive Personalizationcited by 9
  11. 2024Co-clustering for Federated Recommender Systemcited by 9
  12. 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Healthcited by 8
  13. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  14. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  15. 2023FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimizationcited by 7
  16. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  17. 2022Global Convergence of Federated Learning for Mixed Regressioncited by 6
  18. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  19. 2022Federated Learning with Server Learning: Enhancing Performance for Non-IID Datacited by 6
  20. 2023Dynamic Heterogeneous Federated Learning with Multi-Level Prototypescited by 6
  21. 2024Influence-Oriented Personalized Federated Learningcited by 6
  22. 2021Efficient Federated Learning for AIoT Applications Using Knowledge Distillationcited by 5
  23. 2023Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clusteringcited by 5
  24. 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
  25. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  26. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  27. 2021Federated Learning: Issues in Medical Applicationcited by 4
  28. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  29. 2023StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participationcited by 4
  30. 2023Federated cINN Clustering for Accurate Clustered Federated Learningcited by 4
  31. 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learningcited by 4
  32. 2025FedMerge: Federated Personalization via Model Mergingcited by 4
  33. 2022Federated Learning with Intermediate Representation Regularizationcited by 3
  34. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  35. 2023A review of federated learning in renewable energy applications: Potential, challenges, and future directionscited by 3
  36. 2024CCFC: Bridging Federated Clustering and Contrastive Learningcited by 3
  37. 2024CCFC++: Enhancing Federated Clustering through Feature Decorrelationcited by 3
  38. 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learningcited by 3

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