Title: FedCluster: a global user profile generation method based on vertical federated clustering

Authors: Zheng Huo; Ping He; Lisha Hu

Addresses: Information Technology School, Hebei University of Economics and Business, Shijiazhuang, Hebei Province, China ' Information Technology School, Hebei University of Economics and Business, Shijiazhuang, Hebei Province, China ' Information Technology School, Hebei University of Economics and Business, Shijiazhuang, Hebei Province, China

Abstract: Federated learning can serve as a basis to solve the data island problem and data privacy leakage problem in distributed machine learning. This paper proposes a privacy-preserving algorithm referred to as FedCluster, to construct a global user profile via vertical federated clustering. The traditional k medoids algorithm was then extended to the federated learning architecture to construct the user profiles on vertical segmented data. The main interaction parameter between the participants and the server was the distance matrix from each point to the k medoids. Differential privacy was adopted to protect the privacy of the participant data during the exchange of training parameters. We conducted experiments on a real-world dataset. The results revealed that the precision of FedCluster reached 81.87%. The runtime exhibited a linear increase with an increase in the dataset size and the number of participants, which indicates a high performance in terms of precision and effectiveness.

Keywords: federated learning; footrule distance; k medoids clustering; order preserving encryption; OPE.

DOI: 10.1504/IJCSE.2024.137277

International Journal of Computational Science and Engineering, 2024 Vol.27 No.2, pp.123 - 132

Received: 28 Jul 2022
Received in revised form: 22 Oct 2022
Accepted: 29 Oct 2022
Published online: 11 Mar 2024 *

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