Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

Pokhrel, Shiva Raj and Choi, Jinho 2020, Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges, IEEE Transactions on Communications, vol. 68, no. 8, pp. 4734-4746, doi: 10.1109/tcomm.2020.2990686.

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Title Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges
Author(s) Pokhrel, Shiva Raj
Choi, JinhoORCID iD for Choi, Jinho orcid.org/0000-0002-4895-6680
Journal name IEEE Transactions on Communications
Volume number 68
Issue number 8
Start page 4734
End page 4746
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020-08
ISSN 0090-6778
1558-0857
Keyword(s) Science & Technology
Technology
Engineering, Electrical & Electronic
Telecommunications
Engineering
Delays
Blockchain
Computational modeling
Training
Autonomous vehicles
Servers
Machine learning
On-vehicle machine learning
federated learning
delay analysis
consensus delay
low delay
INTERNET
PERFORMANCE
THINGS
Summary 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 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.
Language eng
DOI 10.1109/tcomm.2020.2990686
Indigenous content off
Field of Research 0804 Data Format
0906 Electrical and Electronic Engineering
1005 Communications Technologies
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141070

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