Client Selection with Bandwidth Allocation in Federated Learning

Federated learning (FL) is emerging as a promising paradigm for achieving distributed machine learning while protecting users' privacy. The accuracy and convergence speed of the global model benefit from involving as many clients as possible during the model training. On the other hand, the scarcity of wireless spectrum restricts the number of clients involved at each round. In this paper, we aim to maximize the number of participating clients in each round with fixed wireless bandwidth. Instead of assuming that the prior information about wireless channel state is available, we consider a more practical scenario under the absence of prior information. We first reformulate the client selection problem with limited bandwidth as a combinatorial multi-armed bandit (CMAB) problem and then propose an online learning algorithm with elegant bandwidth allocation based on the framework of combinatorial upper confidence bound. The proposed algorithm can make full use of the scarce bandwidth to increase the number of involved clients in each round and minimize the training latency for a given training accuracy. Numerical results validate the efficiency of the proposed algorithm.