FedCCS: Efficient Federated Learning with Clustering-Based Client Selection and Bandwidth Allocation

Federated learning (FL) has emerged as the most promising distributed machine learning training framework due to its advantages in efficiency, privacy preservation, and scalability. In the practical deployment, FL usually faces the system heterogeneity, data heterogeneity, and limited communication resources. Many works attempt to address the above challenges by client selection, but seldom consider the issues of missing classes in training samples and communication resource allocation, which leads to poor training performance. In this paper, we propose an efficient FL framework with clustering-based client selection and bandwidth allocation, called FedCCS. Specifically, FedCCS first clusters clients based on the local label distribution. By ensuring clients from each cluster participate in training, a wider range of sample classes are covered to mitigate data heterogeneity effects. Furthermore, considering system heterogeneity and limited communication resources, we develop an iterative-based joint optimization algorithm for client selection and bandwidth allocation to minimize latency. Experimental results on both simulations and real-world prototypes show that, compared to other methods, FedCCS can significantly reduce latency and improve the stability of model performance during the training process.