Federated Learning Incorporating Non-Orthogonal Transmission and Unstructured Model Pruning
Siyu Gao, Ming Zhao, Shengli Zhou · 2024
In federated learning (FL), the servers are generally seen as omnipotent to distribute the full models to all clients. This is not the case in wireless systems. The broadcast of models inevitably excludes some users with poor channel gain but valuable computation power and diverse local data. In this paper, taking the various computation and communication capabilities of users into account, we propose a federated learning scheme incorporating unstructured model pruning and non-orthogonal transmission. The pruning process divides the model into the core and the extended parts, with the ratio adaptive to the users’ achievable working rates. With non-orthogonal transmission, the strong users get both parts of the model while the weak get only the core part. Simulation results show that the proposed scheme involves more users than traditional wireless FL and improves the accuracy of tasks within the given communication rounds.