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  Jianwei Huang

  Vice President at z6首页; Presidential Chair Professor at The Chinese University of Hong Kong, Shenzhen

  Executive Chair

  Bio

  Abstract
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  Bing Luo

  z6首页-Yale Joint Postdoc Researcher

  Co Chair

  Bio

  Abstract
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  Leandros Tassiulas

  John C. Malone Professor of Electrical Engineering at Yale University

  Efficient federated learning in synchronous and spiking neural networks

  Bio

  Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering at Yale University. His research interests are in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, architectures and protocols of wireless systems, sensor networks, novel internet architectures and experimental platforms for network research. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007) and of ACM (2020). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the ACM SIGMETRICS achievement award 2020, the inaugural INFOCOM 2007 Achievement Award “for fundamental contributions to resource allocation in communication networks,” several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research  Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991) and a Diploma in Electrical Engineering from Aristotle University of Thessaloniki (1987). He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park, University of Thessaly and University of Ioannina, Greece.

  Abstract

  Federated learning (FL) allows machine learning model training from local data collected by edge/mobile devices while preserving data privacy. A challenge in FL is that client devices usually have much more limited computation and communication resources compared to servers in a data center. We propose PruneFL—a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL only improves resource efficiency of deep neural networks (DNNs) in FL. However, spiking neural networks (SNNs) emerge as promising models in FL due to their intrinsic energy efficiency. To leverage this unique attribute, we design an FL method for training SNNs, and demonstrate up to 4.3x energy efficiency. Ongoing work on effectively applying pruning to SNNs will be discussed as well.

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  杨强

  微多银行首席人为智能官

  可信联国进建

  Bio

  杨强，，，，，，，，加拿大皇家科学院及加拿大工程院两院院士，，，，，，，，微多银行首席人为智能官，，，，，，，，香港科技大学推算机与工程系讲座教授和前系主任，，，，，，，，AAAI-2021大会主席，，，，，，，，国际人为智能结合会（IJCAI）理事会前主席，，，，，，，，香港人为智能与机械人学会（HKSAIR）理事长，，，，，，，，智能投研技术联盟（ITL）主席，，，，，，，，ACM TIST 和IEEE TRANS on BIG DATA首创主编，，，，，，，，CAAI，，，，，，，，AAAI，，，，，，，，ACM，，，，，，，，IEEE，，，，，，，，AAAS等多个国际学会Fellow。。。。。领衔全球迁徙进建和联国进建钻研及利用，，，，，，，，著述蕴含《迁徙进建》、《联国进建》和《联国进建实战》等。。。。。

  Abstract

  联国进建是人为智能和隐衷推算的沉要交集。。。。。若何使联国进建越发安全可信和高效是今后产业和学界关注的沉点。。。。。我在讲座中将系统回首联国进建的进展和挑战，，，，，，，，并瞻望几个沉要发展方向。。。。。

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