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

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.