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

Introduction: This article studies a new clustering-based federated learning algorithm that leverages Kullback-Leibler (KL) divergence to tackle heterogeneous data in wireless sensing environments. Methods: Firstly, highdimensional heterogeneous data is subjected to principal component analysis to generate dimension-reduced representations, thereby reducing computational complexity. Secondly, the KL divergence distances between each pair of clients are calculated, followed by clustering according to the minimum threshold. The new KL divergence distance between the aggregated clients and others is taken as the average of the two. Finally, the federated learning training is conducted within each cluster to obtain a personalized model based on the classic wireless datasets. Results and Discussion: 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.