FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective. (arXiv:2110.03061v3 [cs.LG] UPDATED)

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 …

arxiv federated learning learning perspective