FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective

Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters puts a high burden on FL practitioners since various applications prefer different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements of FL training. FedTune is lightweight and flexible, achieving 4.18%-22.48% improvement for different datasets compared to fixed FL hyper-parameters. FedTune is available at \url{https://github.com/dtczhl/FedTuning}.