A new clustered federated learning algorithm for heterogeneous data in high-precision wireless sensing

After the personalized models are tested, clients are reclustered and the models are updated-that is, a series of iterative operations, the optimal number of clusters and recognition accuracy are obtained. The test results show that the proposed algorithm based on KL divergence has higher recognition accuracy than several reported ones.