  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.
