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arXiv · 2211.16669

FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning

Young Geun Kim·Carole-Jean Wu
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Abstract

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.

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Young Geun Kim, Carole-Jean Wu. 2022-11-30. FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning. https://doi.org/10.1109/iiswc55918.2022.00020

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