Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective

29 Sep 2021  ·  Huanle Zhang, Mi Zhang, Xin Liu, Prasant Mohapatra, Michael DeLucia ·

Federated Learning (FL) is a distributed model training paradigm that preserves clients' data privacy. FL hyper-parameters significantly affect the training overheads in terms of time, computation, and communication. 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 FedTuning, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements of FL training. FedTuning is lightweight and flexible, achieving an average of 41% improvement for different training preferences on time, computation, and communication compared to fixed FL hyper-parameters.

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