File(s) under permanent embargo

A Decentralized Federated Learning Approach for Connected Autonomous Vehicles

conference contribution
posted on 01.01.2020, 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.

History

Event

Wireless Communications and Networking Conference Workshops (2020 : Seoul, South Korea)

Pagination

1 - 6

Publisher

IEEE

Location

Seoul, South Korea

Place of publication

Piscataway, N.J.

Start date

06/04/2020

End date

09/04/2020

ISBN-13

9781728151786

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

WCNCW 2020 : Proceedings of the 2020 IEEE Wireless Communications and Networking Conference Workshops