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Efficient Federated Learning for Internet of Vehicles
Professor of The University of Electro-Communications, Tokyo, Japan (UEC)
Federated learning (FL) is a promising paradigm for achieving distributed intelligence by protecting user privacy in Internet-of-Vehicles (IoV). Considering limited computing and communication resources, it is important to compress learning models and select appropriate clients from a huge
number of users to participate in the training process. This talk discusses two approaches to empower federated learning in IoV. First approach uses a knowledge distillationbased scheme to compress local models to reduce the communication overhead between FL client and the central server. Second approach uses a fuzzy logic based client selection scheme to improve the learning efficiency.
Celimuge Wu received his PhD degree from The University of Electro-Communications, Japan. He is currently a professor and the director of Meta-Networking Research Center, The University of ElectroCommunications. His research interests include Vehicular Networks, Edge Computing, IoT, Intelligent Transport Systems, and AI for Wireless Networking and Computing. He serves as an associate editor of IEEE Transactions on Network Science and Engineering, IEEE Transactions on Green Communicationsand Networking, and IEEE Open Journal of the Computer Society. He is Vice Chair (Asia Pacific) of IEEE Technical Committee on Big Data (TCBD), and the chair of IEEE TCGCC Special Interest Group on Green Internet of Vehicles. He is a
recipient of 2021 IEEE Communications Society Outstanding Paper Award, 2021 IEEE Internet of Things Journal Best Paper Award, IEEE Computer
Society 2020 Best Paper Award and IEEE Computer Society 2019 Best Paper Award Runner-Up. He is/has been a symposium/track Co-Chair IEEE ICC
2024 and IEEE ICC 2023, a TPC Co-Chair of IEEE International Smart Cities Conference 2021, a TPC Co-chair of the 2021 IEEE Autonomous Driving AI
Test Challenge, a General Chair of ICT-DM 2021, a TPC Co-chair of Wireless Days 2021, a Track Co-Chair of IEEE VTC-Spring 2020, a Track Co-Chair of ICCCN 2019, and a Track Chair of IEEE PIMRC 2016. He is a senior member of IEEE.