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Federated Deep Learning (FDL) is helping to realize distributed machinelearning in the Internet of Vehicles (IoV). However, FDL's global model needsmultiple clients to upload learning model parameters, thus still existingunavoidable communication overhead and data privacy risks. The recentlyproposed Swarm Learning (SL) provides a decentralized machine-learning approachuniting edge computing and blockchain-based coordination without the need for acentral coordinator. This paper proposes a Swarm-Federated Deep Learningframework in the IoV system (IoV-SFDL) that integrates SL into the FDLframework. The IoV-SFDL organizes vehicles to generate local SL models withadjacent vehicles based on the blockchain empowered SL, then aggregates theglobal FDL model among different SL groups with a proposed credibility weightsprediction algorithm. Extensive experimental results demonstrate that comparedwith the baseline frameworks, the proposed IoV-SFDL framework achieves a 16.72%reduction in edge-to-global communication overhead while improving about 5.02%in model performance with the same training iterations.

A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles