论文标题
FEDGPO：异质性 - 感知全球参数优化，以进行有效的联合学习
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning
论文作者
论文摘要
联邦学习（FL）已成为解决机器学习培训中隐私泄漏风险的解决方案。这种方法允许各种移动设备可以协作训练机器学习模型，而无需与云共享原始的启动培训数据。但是，由于系统/数据异质性和运行时差异，FL的有效边缘部署是具有挑战性的。本文通过考虑上述挑战，在保证模型收敛的同时优化了FL用例的能源效率。我们根据强化学习提出了FedGPO，该学习如何识别每个FL聚合圆形的最佳全局参数（B，E，K），以适应系统/数据异质性和随机运行时差异。在我们的实验中，FedGPO将模型收敛时间提高了2.4倍，并在基线设置上分别提高了3.6倍的能源效率。
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.
