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.

翻译：联邦学习(FL)已成为解决机器学习培训中隐私泄漏风险的一种解决办法,这种方法使各种移动设备能够合作培训机器学习模式,而不必与云层分享原始设备培训数据,然而,由于系统/数据差异和运行时间差异,FL的有效边缘部署具有挑战性。本文通过考虑上述挑战,优化FL使用案例的能源效率,同时保证模型趋同。我们提议FedGPO基于强化学习,学习如何为适应系统/数据差异和随机运行时间差异的每轮FL集合确定最佳全球参数(B、E、K)。在我们的实验中,FedGPO将模型的趋同时间分别改进2.4倍,并在基线环境中实现3.6倍的能源效率。