时 间：2023年08月31日10:00-11:00
摘要：
在联邦学习中，模型评估变成了一个具有挑战性的问题，称之为联邦评估。因为客户端不会公开其原始数据以保护数据隐私。联邦评估在客户端选择、激励机制设计、恶意攻击检测等方面发挥着重要作用。在本文中，我们首次对现有的联邦评估方法进行了全面的调查。此外，我们探讨了联邦评估在增强FL性能方面的各种应用，并最终提出了未来的研究方向，展望了一些挑战。
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.
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