Joint Client Selection and Bandwidth Allocation of Wireless Federated Learning by Deep Reinforcement Learning

Federated Learning (FL) is a promising paradigm for massive data mining service while protecting users' privacy. In wireless federated learning networks (WFLNs), limited communication resources and heterogeneity of user devices have essential impacts on training efficiency of FL, hence it is critical to select clients and allocate network bandwidths among them in each learning round to improve the training efficiency. In this article, we formulate the joint client selection and bandwidth allocation optimization problem as a MDP process and design a FL framework CSBWA to solve it. CSBWA relies on DRL-based REINFORCE algorithm to automatically perform effective policy based on observed information, e.g., client states, historical bandwidths, and feedback rewards. It is able to achieve lower time cost and energy consumption with long-term FL performance guarantee by jointly optimizing the client selection and bandwidth allocation. Experimental results show the effectiveness of CSBWA in reducing time cost and energy consumption while guaranteeing model performance of wireless federated learning compared with existing state-of-art methods.