TL;DR
FedTune is an automatic hyper-parameter tuning algorithm for federated learning that optimizes training efficiency based on system requirements, reducing manual effort and improving performance across datasets.
Contribution
The paper introduces FedTune, a lightweight and flexible method for automatic hyper-parameter tuning in federated learning tailored to diverse system needs.
Findings
Achieves 8.48%-26.75% improvement over fixed hyper-parameters
Reduces manual hyper-parameter selection burden
Adapts to various system requirements
Abstract
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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