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

Autor:	Kim, Young Geun, Wu, Carole-Jean
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing 68Txx
Druh dokumentu:	Working Paper
DOI:	10.1109/IISWC55918.2022.00020
Popis:	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. Comment: 12 pages, 12 figures, IEEE International Symposium on Workload Characterization (IISWC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16669 Zobrazit plný text záznamu View this record from Arxiv