Secure and Efficient Hierarchical Decentralized Learning for Internet of Vehicles

Decentralized machine learning enables multiple devices to train a global model collaboratively and is a promising paradigm to realize ubiquitous intelligence for the Internet of Vehicles (IoV). Existing work mainly focused on either the data privacy protection techniques or efficient topology orchestration of decentralized machine learning. However, these techniques cannot be directly applied to IoV due to possible accuracy degradations and insufficient topology adaptability, not to mention the joint secure and efficient decentralized learning designs. This paper proposes a secure and efficient hierarchical decentralized learning framework for IoV networks with multiple fog nodes and mobile vehicles. The proposed framework combines federated learning and distributed consensus for vehicle-fog and inter-fog collaborative learning, respectively, and integrates masking with local training to protect data privacy. We propose the network-level masking mechanism and consensus matrix optimization for signaling-efficient implementations in IoV. The network-level masking can eliminate the masking pairing requirements of inter-fog handover of mobile vehicles and is proved to be canceled via distributed consensus. Experimental results on two popular datasets validate the superiority of the proposed framework in terms of learning accuracy, data protection, and signaling efficiency, compared to the existing approaches.