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Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges
journal contribution
posted on 2020-08-01, 00:00 authored by Shiva PokhrelShiva Pokhrel, Jinho ChoiJinho ChoiWe 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 oVML 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 (e.g., 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. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes. 1 However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future reserach directions.
History
Journal
IEEE Transactions on CommunicationsVolume
68Issue
8Pagination
4734 - 4746Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
0090-6778eISSN
1558-0857Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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Keywords
Science & TechnologyTechnologyEngineering, Electrical & ElectronicTelecommunicationsEngineeringDelaysBlockchainComputational modelingTrainingAutonomous vehiclesServersMachine learningOn-vehicle machine learningfederated learningdelay analysisconsensus delaylow delayINTERNETPERFORMANCETHINGSData Format
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