mlsanityData to 7 Oct 2026

Secure Federated Clustering

arXiv 2205.15564 ↗First posted 31 May 2022cs.LG cs.CR cs.ITcited by 10

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Abstract (arXiv)

We consider a foundational unsupervised learning task of k-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achieves 1) universal performance: no performance loss compared with clustering over centralized data, regardless of data distribution across clients; 2) data privacy: each client's private data and the cluster centers are not leaked to other clients and the server. In SecFC, the clients perform Lagrange encoding on their local data and share the coded data in an information-theoretically private manner; then leveraging the algebraic structure of the coding, the FL network exactly executes the Lloyd's k-means heuristic over the coded data to obtain the final clustering. Experiment results on synthetic and real datasets demonstrate the universally superior performance of SecFC for different data distributions across clients, and its computational practicality for various combinations of system parameters. Finally, we propose an extension of SecFC to further provide membership privacy for all data points.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

22 of 53 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  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 Federated Learning: A Meta-Learning Approachcited by 309
  12. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
10 more
  1. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  2. 2016Agnostic Estimation of Mean and Covariancecited by 131
  3. 2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 106
  4. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  5. 2018ClusterGAN : Latent Space Clustering in Generative Adversarial Networkscited by 75
  6. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  7. 2020Diverse Image Generation via Self-Conditioned GANscited by 54
  8. 2020Dynamic Clustering in Federated Learningcited by 27
  9. 2013Distributed k-Means and k-Median Clustering on General Topologiescited by 24
  10. 2020Differentially Private Set Unioncited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 2; 2026: 1.

10 papers.

  1. 2022Machine Unlearning of Federated Clusterscited by 21
  2. 2023Differentially Private Federated Clustering over Non-IID Datacited by 7
  3. 2023Federated K-Means Clustering via Dual Decomposition-based Distributed Optimizationcited by 2
  4. 2024Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributionscited by 1
  5. 2024Jigsaw Game: Federated Clusteringcited by 1
  6. 2022Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systemscited by 0
  7. 2023Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computationcited by 0
  8. 2025OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstructioncited by 0
  9. 2025LocalKMeans: Convergence of Lloyd's Algorithm with Distributed Local Iterationscited by 0
  10. 2026Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numberscited by 0