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Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions

arXiv 2408.10664 ↗First posted 20 Aug 2024cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has been widely studied in supervised contexts, its application to unsupervised learning remains underdeveloped. This work introduces FedCRef, a novel unsupervised federated learning method designed to uncover all underlying data distributions across decentralized clients without requiring labels. This task, known as Federated Clustering, presents challenges due to heterogeneous, non-uniform data distributions and the lack of centralized coordination. Unlike previous methods that assume a one-cluster-per-client setup or require prior knowledge of the number of clusters, FedCRef generalizes to multi-cluster-per-client scenarios. Clients iteratively refine their data partitions while discovering all distinct distributions in the system. The process combines local clustering, model exchange and evaluation via reconstruction error analysis, and collaborative refinement within federated groups of similar distributions to enhance clustering accuracy. Extensive evaluations on four public datasets (EMNIST, KMNIST, Fashion-MNIST and KMNIST49) show that FedCRef successfully identifies true global data distributions, achieving an average local accuracy of up to 95%. The method is also robust to noisy conditions, scalable, and lightweight, making it suitable for resource-constrained edge devices.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 20 Aug 2024.

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21 of 30 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  8. 2017Federated Multi-Task Learningcited by 669
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  10. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  11. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  12. 2017EMNIST: an extension of MNIST to handwritten letterscited by 212
9 more
  1. 2019A Survey on Distributed Machine Learningcited by 187
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  3. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  4. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  7. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  8. 2023Federated Learning without Full Labels: A Surveycited by 12
  9. 2022Anomaly Detection through Unsupervised Federated Learningcited by 8

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