Abstract

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.

Bio.

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.