主題：Communication Efficient Federated Learning
主要內容：Federated learning is a new distributed learning paradigm that can preserve data privacy in machine learning. One of the main challenges in federated learning is to reduce the communication costs for transmitting model parameters between local devices and the central server and vice versa. This talk presents some most recent work on communication efficient federated learning, including constructing compact local models, introducing layer-wise asynchronous parameter update, and using ternary quantization. At the end of the talk, future directions of research in federated learning will be briefly discussed.
專家姓名：金耀初
工作單位：University of Surrey
專長和學術成就：智能計算
專家簡介：Yaochu Jin received the BSc, MSc, and PhD degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001. He is currently a Distinguished Chair Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, and “Changjiang Distinguished Visiting Professor”, Northeastern University, China. His main research interests include data-driven surrogate-assisted evolutionary optimization, trustworthy machine learning, multi-objective evolutionary learning, swarm robotics, and evolutionary developmental systems. Dr Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2018 and 2020 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2014, 2016, and 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award. He is named as a Highly Cited Researcher 2019 by the Web of Science Group. He is a Fellow of IEEE.
時間：2020-11-16 16:00:00
地點：騰訊會議