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News and Resources for Members of the IEEE Signal Processing Society
Title: Decentralized Federated Learning: Balancing Communication and Computing Costs
Date: 15 February 2023
Time: 10:00 AM Eastern (New York time)
Duration: Approximately 1 Hour
Presenters: Mr. Wei Liu, Dr. Li Chen and Dr. Wenyi Zhang
Based on the IEEE Xplore® article: Decentralized Federated Learning: Balancing Communication and Computing Costs
Published: IEEE Transactions on Signal and Information Processing over Networks, February 2022, available in IEEE Xplore®
Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. This webinar will introduce a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. We will first describe a general system model of DFL, and then present the proposed DFL framework along with several existing learning strategies of DFL. We will show the theoretical convergence performance for the proposed DFL algorithm without the assumption of convex objectives. Furthermore, in this talk we will include a compressed communication scheme based on the proposed DFL framework, named C-DFL, to improve communication efficiency. Finally, we will present experiment results based on MNIST and CIFAR-10 datasets, illustrating the superiority of DFL over traditional decentralized SGD methods and demonstrating that C-DFL further enhances communication efficiency.
Wei Liu received the B.E. degree in electronic information engineering in 2018 from the University of Science and Technology of China, Hefei, China, where he is currently working toward the Ph.D. degree with the Department of Electronic Engineering and Information Science. His research interests include distributed machine learning and wireless communications.
Li Chen received the B.E. in electrical and information engineering from Harbin Institute of Technology, Harbin, China, in 2009 and the Ph.D. degree in electrical engineering from the University of Science and Technology of China, Hefei, China, in 2014.
Dr. Chen is currently an Associate Professor with the Department of Electronic Engineering
and Information Science, University of Science and Technology of China. His research interests include integrated communication and computation, integrated sensing and communication and wireless IoT networks.
Wenyi Zhang (SM'11) received the B.E. degree in Automation from Tsinghua University, Beijing, China, in 2001 and the Ph.D. degree in Electrical Engineering from University of Notre Dame, Notre Dame, IN, USA, in 2006.
He was a Postdoctoral Research Associate with University of Southern California, Los Angeles, CA, USA, from 2006 to 2008. He was a System Engineer with Qualcomm Corporate Research and Development, San Diego, CA, USA, from 2008 to 2009, and since 2010 he has been with University of Science and Technology of China, Hefei, China, where he is currently a professor. His research interest includes wireless communications, information theory, and statistical signal processing.
Dr. Zhang has received 100 Talents Program of Chinese Academy of Sciences, Excellent Young Scholar of National Natural Science Foundation of China, Changjiang (Yangtze River) Young Scholar of Ministry of Education of P.R.C., Young New Star of Information Theory Society of Chinese Institute of Electronics, and IEEE ComSoc Asia-Pacific Outstanding Young Researcher. He was an Editor for IEEE Communications Letters and IEEE Transactions on Wireless Communications. He is a Vice Chair of IEEE ComSoc Nanjing Chapter.
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