FEDERATED LEARNING METHOD FOR k-means CLUSTERING ALGORITHM

摘要

Disclosed is a federated learning method for a k-means clustering algorithm. Horizontal federated learning includes the following steps: 1) initializing K clusters, and distributing, a local sample to a cluster closest to the sample; 2) calculating a new cluster center of the cluster; and 3) if the cluster center changes, then returning to step 1). Vertical federated learning includes the following steps: 1) running, the k-means clustering algorithm locally to obtain T local clusters and intersecting to obtain TL new clusters, or running an AP clustering algorithm to obtain Ti clusters and intersecting to obtain Πi=1LTi new clusters; 2) taking TL (Πi=1LTi) new cluster centers as input samples, and initializing the K clusters; 3) distributing each sample to the cluster closest to the sample; 4) calculating a new cluster center of the cluster; and 5) if the cluster center changes, then returning to step 3).