Paper 2022/682

Secure Federated Clustering

Songze Li, The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology
Sizai Hou, The Hong Kong University of Science and Technology (Guangzhou)
Baturalp Buyukates, University of Southern California
Salman Avestimehr, University of Southern California

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 central- ized 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.

Available format(s)
Cryptographic protocols
Publication info
Federated Learning Clustering Data Privacy Membership Privacy Secret Sharing
Contact author(s)
songzeli8824 @ gmail com
shouac @ connect ust hk
avestime @ usc edu
2022-05-31: approved
2022-05-31: received
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Creative Commons Attribution-NonCommercial-NoDerivs


      author = {Songze Li and Sizai Hou and Baturalp Buyukates and Salman Avestimehr},
      title = {Secure Federated Clustering},
      howpublished = {Cryptology ePrint Archive, Paper 2022/682},
      year = {2022},
      note = {\url{}},
      url = {}
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