A credibility-aware swarm-federated deep learning framework in internet of vehicles

Abstract

Although Federated Deep Learning (FDL) enables distributed machine learning in the Internet of Vehicles (IoV), it requires multiple clients to upload model parameters, thus still existing unavoidable communication overhead and data privacy risks. The recently proposed Swarm Learning (SL) provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination. A Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework is proposed in this paper. The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL, then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm. Extensive experimental results show that compared with the baseline frameworks, the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%, while the model performance improves by about 5.02% for the same training iterations.

Keywords

Swarm learning / Federated deep learning / Internet of vehicles / Privacy / Efficiency

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