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

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arxiv 2110.03061 v6 pith:HL2CXWEO submitted 2021-10-06 cs.LG

Huanle Zhang , Mi Zhang , Xin Liu , Prasant Mohapatra , Michael DeLucia This is my paper · ORCID
classification cs.LG
keywords hyper-parametersfedtunetrainingapplicationsautomaticcomputationdifferentfederated
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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 8.48%-26.75% improvement for different datasets compared to fixed FL hyper-parameters.

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