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

By Shiva Raj Pokhrel, Jinho Choi. IEEE Transactions on Communications, 2020

471 citations recorded by the source corpus.

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.1An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020 [1].

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/3cb02737814fccf88287aa54371f844c32207a4d

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Citations in the corpus

Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, methodology): “This mechanism has been utilized in several studies, such as [219] and [220].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “1) Problem Statement: Resource allocation is important to support the operations of FLchain.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “[27] Vehicular traffic planning DFL NN Vehicles Vehicles A decentralized FL approach with blockchain for vehicular networks.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The block generation rate was optimized by considering the communication, computation and consensus delays in the blockchain enabled secure edge learning systems [253], [252].”