  Federated Deep Learning (FDL) is helping to realize distributed machine
learning in the Internet of Vehicles (IoV). However, FDL's global model needs
multiple clients to upload learning model parameters, thus still existing
unavoidable communication overhead and data privacy risks. The recently
proposed Swarm Learning (SL) provides a decentralized machine-learning approach
uniting edge computing and blockchain-based coordination without the need for a
central coordinator. This paper proposes a Swarm-Federated Deep Learning
framework in the IoV system (IoV-SFDL) that integrates SL into the FDL
framework. 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 proposed credibility weights
prediction algorithm. Extensive experimental results demonstrate that compared
with 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.
