先端ワイヤレス・コミュニケーション研究センター(AWCC)は，IEEE Communications Society (ComSoc) Distinguished Lecturerである台湾国立清華大学のDr. Y.-W. Peter Hongをお招きして，IEEE ComSoc Distinguished
Lecture (DL)を実現する運びとなりました．
Dr. Hongは分散信号処理や，IoT通信，無線通信への機械学習の応用といった研究分野において，世界的に活躍されている著名な研究者のお一人であります．
学生の皆さんにとっては，第一線でご活躍されている研究者からお話を聞くことのできる貴重な機会であるので，奮ってご参加くださいますようお願い申し上げます．
日時： 2023年11月28日（火）13:00~14:00
場所： 電気通信大学アライアンスセンター100周年記念ホール
参加費： 無料
予約： 参加人数把握のため，下記URLよりセミナー開始前の11月28日（火）9:00までにお申し込みください．
申し込み用Google
Form：https://forms.gle/tDLg3ktYNJJJ9dJY8
もしGoogle Formにアクセスできない場合には，以下の情報をadachi[アット]awcc.uec.ac.jpまでお送りください．
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＊お名前：
＊ご連絡先（メールアドレス）：
＊ご所属：
＊所属研究室名（電通大生のみ）：
＊IEEE
Membership種別（該当するものを残してください）：
– IEEE ComSoc Member（Student member含む）
– IEEE Member（Student Member含む）
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講師：
Dr. Y.-W. Peter Hong, Professor, National
Tsing Hua University, Taiwan
講演タイトル
Federated Knowledge Caching and In-Network
Learning over Wireless Networks
※講演は英語で行われ通訳はつきません
講演概要：
Most modern applications in artificial
intelligence (AI) rely on a centralized data center to perform the training and
inference over large datasets. Distributed (or federated) learning techniques
allow the learning to be moved from large data centers to distributed edge
entities, where users have more control of both their own data and
computational resources. The local entities can collaborate to perform complex
learning or inference tasks, but the frequent message exchanges required may
cause a significant increase in the wireless traffic, which must be properly
addressed. In this talk, I will discuss the impact of distributed learning over
wireless networks and introduce two of our recent works in this direction. By
focusing on cellular networks, we first examine the optimization of wireless
resources for federated learning between local users and the base-station, and
introduce a new knowledge caching framework to facilitate both training and
access of machine learning models by these users. The model caching strategy
must take into consideration the training efficiency, channel conditions, and
the user preference. Then, for internet-of-things or wireless sensor networks,
we propose a novel in-network learning framework, where low-cost sensor devices
(each hosting only a small neural network) may collaborate to form a deep
neural network over wireless multihop links. We utilize over-the-air
computation to improve the communication efficiency, and network
reconfiguration to adapt to varying sensor deployments and inference tasks.
Through these works, we show that the new traffic type generated by distributed
learning requires new wireless designs and considerations that are different
from conventional voice and data traffic.
講師略歴：
Y.-W. Peter Hong received his B.S. degree
from National Taiwan University in 1999, and his Ph.D. degree from Cornell
University in 2005, both in electrical engineering. He is a Distinguished
Professor of the Institute of Communications Engineering and Department of
Electrical Engineering at National Tsing Hua University, Hsinchu, Taiwan,
Associate Dean of the Colleges of EECS, and Director of the LiteOn-NTHU Joint
Research Center. His research interests include AI/ML in wireless
communications, signal processing for sensor networks, UAV communications,
distributed learning and optimization, and physical layer secrecy.
Dr. Hong currently serves as Senior Area
Editor of IEEE Transactions on Signal Processing. He is also a Distinguished
Lecturer of IEEE Communications Society (2022-2023) and the Vice Director of
the IEEE ComSoc Asia-Pacific Board (2022-2023). He received several awards for his
research contributions, including the 2010 IEEE ComSoc Asia-Pacific Outstanding
Young Researcher Award, 2011 Y. Z. Hsu Scientific Paper Award, 2011 National
Science Council Wu Ta-You Memorial Award, 2012 CIEE Outstanding Young
Electrical Engineer Award, 2018 National Science and Technology Council (NSTC)
Outstanding Research Award, 2022 CIEE Outstanding Electrical Engineering
Professor Award, and 2022 NSTC Outstanding Research Award.
問い合わせ先：先端ワイヤレス・コミュニケーション研究センター(AWCC) 安達宏一(adachi[アット]awcc.uec.ac.jp)
