报告人：Prof. Dapeng Oliver Wu,IEEE Fellow,Department of Computer Science,City University of Hong Kong (香港城市大学)

报告时间：2024年07月05日 (星期五) 10:30-11:30 am

报告地点：信息科学与工程学院624会议室

报告摘要: The goal of federated learning is to preserve data privacy when training Artificial Intelligence (AI) systems, while continual learning is to enable an AI system to acquire new skills without forgetting old skills.To combine the capabilities of federated learning and continual learning, federated continual learning (FCL) arises.But before FCL can enjoy the benefits of both federated learning and continual learning, FCL needs to be able to effectively transfer knowledge across different clients and across various tasks. Current FCL methods mainly focus on avoiding interference between tasks, thereby overlooking the potential of knowledge transfer across tasks learned by different clients in separated time intervals. To address this issue, in this talk, I will present a Prompt-based Knowledge Transfer FCL algorithm, to effectively foster the transfer of knowledge encapsulated in prompts between various sequentially learned tasks and clients.

联邦学习的目标是在训练人工智能（AI）系统时保护数据隐私，而持续学习的目标是使AI系统能够获得新技能而不忘记旧技能。为了结合联邦学习和持续学习的能力，联邦持续学习（FCL）应运而生。但在FCL享受联邦学习和持续学习的双重益处之前，FCL需要能够有效地跨不同客户端和各种任务传递知识。目前的FCL方法主要侧重于避免任务之间的干扰，因此忽略了不同客户端在不同时间间隔内学习的任务之间的知识转移潜力。为了解决这个问题，在本次演讲中，我将介绍一种基于提示的知识转移FCL算法，以有效促进各种顺序学习任务和客户端之间的提示知识转移。

报告人简介: Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Currently, he is Yeung Kin Man Chair Professor of Network Science, at the Department of Computer Science, City University of Hong Kong.His research interests are in the areas of artificial intelligence, FinTech, communications, image processing, computer vision, signal processing, and biomedical engineering.

He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. He has served as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Cloud Computing, IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, IEEE Signal Processing Magazine, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology.He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.

吴大鹏（Dapeng Oliver Wu）于2003年获得卡内基梅隆大学（Carnegie Mellon University）电气与计算机工程博士学位。目前，他是香港城市大学计算机科学系杨建文网络科学讲座教授。他的研究兴趣包括人工智能、金融科技、通信、图像处理、计算机视觉、信号处理和生物医学工程。

他在2017年获得了佛罗里达大学任期教授奖，2009年获得佛罗里达大学研究基金教授奖，2009年获得美国空军科学研究办公室（AFOSR）青年研究员奖（YIP），2008年获得美国海军研究办公室（ONR）青年研究员奖（YIP），2007年获得国家科学基金会（NSF）职业生涯奖（CAREER），并在2001年获得IEEE视频技术电路与系统学报（CSVT）最佳论文奖，2011年获得GLOBECOM最佳论文奖，2006年获得QShine最佳论文奖。他曾担任IEEE网络科学与工程汇刊主编，IEEE云计算汇刊、IEEE通信汇刊、IEEE网络信号与信息处理汇刊、IEEE信号处理杂志、IEEE视频技术电路与系统汇刊、IEEE无线通信汇刊和IEEE车载技术汇刊的副主编。他曾在2006年至2008年间担任《多媒体进展杂志》创刊主编。他曾担任IEEE INFOCOM 2012技术程序委员会（TPC）主席，并在2016年被选为IEEE车载技术协会杰出演讲者。他是IEEE Fellow。

邀请人：蒋洪波

联系人：胡靖阳        联系电话：13667383125