报告题目：Federated Evaluation in Federated Learning

报告嘉宾：Yipeng Zhou, Senior Lecturer, Macquarie University

时间：2023年8月29日15：00-16：00

地点：致腾楼623会议室

主持人：崔来中教授

报告摘要：

In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.

报告人简介：

Dr Yipeng Zhou is a senior lecturer with School of Computing, Faculty of Science and Engineering at Macquarie University, Australia. He is the recipient of ARC Discover Early Career Researcher Award (DECRA) in 2018. He got his Ph.D. degree from Information Engineering Department of CUHK and Bachelor degree from Department of Computer Science and Technology of University of Science and Technology of China (USTC). His research interests lie in federated learning, privacy protection and networking. He has published more than 100 papers including IEEE INFOCOM, IJCAI, ICNP, IWQoS, IEEE ToN, TDSC, JSAC, TPDS, TMC, TMM, etc.