An Efficient Client Selection for Wireless Federated Learning

Abstract

As a promising distributed learning technology, federated learning (FL) is used in wireless communication to efficiently utilize distributed data. However, statistical heterogeneity is often ignored as a crucial factor affecting wireless federated learning (WFL) performance. Besides, free rider is common in real world. In this paper, we consider the statistical heterogeneity and free rides jointly with limited resources. We first define a new measurement considering the substitutability and wholeness of client, called contribution degree. Then we propose the Contribution Degree-based Client Selection (CDCS) algorithm to improve WFL performance. Experiments validate that the proposed algorithm improves the global model accuracy, achieves fast convergence and reduces total delay.