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A Decentralized Federated Learning Approach for Connected Autonomous Vehicles
conference contributionposted on 2020-01-01, 00:00 authored by Shiva PokhrelShiva Pokhrel, Jinho ChoiJinho Choi
© 2020 IEEE. In this paper, we propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables on-vehicle machine learning without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters, such as the retransmission limit, block size, block arrival rate, and the frame sizes, so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays.
EventWireless Communications and Networking Conference Workshops (2020 : Seoul, South Korea)
Pagination1 - 6
LocationSeoul, South Korea
Place of publicationPiscataway, N.J.
Publication classificationE1 Full written paper - refereed
Title of proceedingsWCNCW 2020 : Proceedings of the 2020 IEEE Wireless Communications and Networking Conference Workshops
CategoriesNo categories selected