气候变化领域集成服务门户(CCPortal): Communication-efficient federated learning

CCPortal

DOI	10.1073/pnas.2024789118
	Communication-efficient federated learning
	Chen M.; Shlezinger N.; Vincent Poor H.; Eldar Y.C.; Cui S.
发表日期	2021
ISSN	00278424
卷号	118 期号:17
英文摘要	Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Federated learning; Machine learning; Wireless communications
语种	英语
scopus关键词	article; machine learning; probability; resource allocation; simulation; velocity; wireless communication
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179757
作者单位	Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, 518172, China; Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, United States; School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel; Faculty of Math and Computer Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
推荐引用方式 GB/T 7714	Chen M.,Shlezinger N.,Vincent Poor H.,et al. Communication-efficient federated learning[J],2021,118(17).
APA	Chen M.,Shlezinger N.,Vincent Poor H.,Eldar Y.C.,&Cui S..(2021).Communication-efficient federated learning.Proceedings of the National Academy of Sciences of the United States of America,118(17).
MLA	Chen M.,et al."Communication-efficient federated learning".Proceedings of the National Academy of Sciences of the United States of America 118.17(2021).