Federated Average Clustering Learning Based on Time-Asynchronous Similarity

To address the challenge of non-independent and identically distributed data in federated learning, clustering federated learning extracts local model features from clients and groups clients with high local model similarity into the same cluster to optimize the global model for the corresponding data distribution. However, current federated clustering algorithms bring additional communication cost for the accuracy of cluster division, which increases the communication burden of some performance-limited edge devices. To address the communication efficiency issue of clustering federated learning, we proposed an optimized communication efficiency federated average clustering learning that uses time weights to select partial clients to participate in training randomly. At the same time, a time-asynchronous similarity calculation method is proposed to improve the accuracy of local model similarity calculation for randomly selected clients. Extensive experimental evaluations show that our federated average clustering learning can achieve or even surpass the model accuracy of existing federated clustering algorithms. In the communication evaluation experiment, we can achieve the specified model accuracy using 5% to 30% of the communication of existing federated clustering algorithms.