adachi[\u30a2\u30c3\u30c8]awcc.uec.ac.jp<\/a>\u307e\u3067\u304a\u9001\u308a\u304f\u3060\u3055\u3044\uff0e<\/p>\n\n\n\n~~~~~~~~~~~~~~~~~~<\/p>\n\n\n\n
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\uff0aIEEE\nMembership\u7a2e\u5225\uff08\u8a72\u5f53\u3059\u308b\u3082\u306e\u3092\u6b8b\u3057\u3066\u304f\u3060\u3055\u3044\uff09\uff1a<\/p>\n\n\n\n
– IEEE ComSoc Member\uff08Student member\u542b\u3080\uff09<\/p>\n\n\n\n
– IEEE Member\uff08Student Member\u542b\u3080\uff09<\/p>\n\n\n\n
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Dr. Y.-W. Peter Hong, Professor, National\nTsing Hua University, Taiwan<\/p>\n\n\n\n
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Federated Knowledge Caching and In-Network\nLearning over Wireless Networks<\/p>\n\n\n\n
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Most modern applications in artificial\nintelligence (AI) rely on a centralized data center to perform the training and\ninference over large datasets. Distributed (or federated) learning techniques\nallow the learning to be moved from large data centers to distributed edge\nentities, where users have more control of both their own data and\ncomputational resources. The local entities can collaborate to perform complex\nlearning or inference tasks, but the frequent message exchanges required may\ncause a significant increase in the wireless traffic, which must be properly\naddressed. In this talk, I will discuss the impact of distributed learning over\nwireless networks and introduce two of our recent works in this direction. By\nfocusing on cellular networks, we first examine the optimization of wireless\nresources for federated learning between local users and the base-station, and\nintroduce a new knowledge caching framework to facilitate both training and\naccess of machine learning models by these users. The model caching strategy\nmust take into consideration the training efficiency, channel conditions, and\nthe user preference. Then, for internet-of-things or wireless sensor networks,\nwe propose a novel in-network learning framework, where low-cost sensor devices\n(each hosting only a small neural network) may collaborate to form a deep\nneural network over wireless multihop links. We utilize over-the-air\ncomputation to improve the communication efficiency, and network\nreconfiguration to adapt to varying sensor deployments and inference tasks.\nThrough these works, we show that the new traffic type generated by distributed\nlearning requires new wireless designs and considerations that are different\nfrom conventional voice and data traffic.<\/p>\n\n\n\n
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Y.-W. Peter Hong received his B.S. degree\nfrom National Taiwan University in 1999, and his Ph.D. degree from Cornell\nUniversity in 2005, both in electrical engineering. He is a Distinguished\nProfessor of the Institute of Communications Engineering and Department of\nElectrical Engineering at National Tsing Hua University, Hsinchu, Taiwan,\nAssociate Dean of the Colleges of EECS, and Director of the LiteOn-NTHU Joint\nResearch Center. His research interests include AI\/ML in wireless\ncommunications, signal processing for sensor networks, UAV communications,\ndistributed learning and optimization, and physical layer secrecy.<\/p>\n\n\n\n
Dr. Hong currently serves as Senior Area\nEditor of IEEE Transactions on Signal Processing. He is also a Distinguished\nLecturer of IEEE Communications Society (2022-2023) and the Vice Director of\nthe IEEE ComSoc Asia-Pacific Board (2022-2023). He received several awards for his\nresearch contributions, including the 2010 IEEE ComSoc Asia-Pacific Outstanding\nYoung Researcher Award, 2011 Y. Z. Hsu Scientific Paper Award, 2011 National\nScience Council Wu Ta-You Memorial Award, 2012 CIEE Outstanding Young\nElectrical Engineer Award, 2018 National Science and Technology Council (NSTC)\nOutstanding Research Award, 2022 CIEE Outstanding Electrical Engineering\nProfessor Award, and 2022 NSTC Outstanding Research Award.<\/p>\n\n\n\n
\u554f\u3044\u5408\u308f\u305b\u5148\uff1a\u5148\u7aef\u30ef\u30a4\u30e4\u30ec\u30b9\u30fb\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u7814\u7a76\u30bb\u30f3\u30bf\u30fc(AWCC)\u3000\u5b89\u9054\u5b8f\u4e00(adachi[\u30a2\u30c3\u30c8]awcc.uec.ac.jp)<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u5148\u7aef\u30ef\u30a4\u30e4\u30ec\u30b9\u30fb\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u7814\u7a76\u30bb\u30f3\u30bf\u30fc(AWCC)\u306f\uff0cIEEE Communications Society (ComSoc) Distinguished Lecturer\u3067\u3042\u308b\u53f0\u6e7e\u56fd\u7acb\u6e05\u83ef\u5927\u5b66\u306eDr. Y.-W […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-926","post","type-post","status-publish","format-standard","hentry","category-1"],"_links":{"self":[{"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/926","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=926"}],"version-history":[{"count":4,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/926\/revisions"}],"predecessor-version":[{"id":930,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/926\/revisions\/930"}],"wp:attachment":[{"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=926"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=926"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.awcc.uec.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=926"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}