Adaptive Network Pruning for Wireless Federated Learning

Abstract

In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bottlenecks. Firstly, the convergence rate and learning latency of the FL system are mathematically analyzed. Then, an optimization problem is formulated to maximize the convergence rate while guaranteeing the learning latency via jointly optimizing the pruning ratio and spectrum allocation. Finally, the experimental results show that the proposed learning scheme can improve the performance of the wireless FL as compared with other conventional schemes.