PR-OppCL: Privacy-Preserving Reputation-Based Opportunistic Federated Learning in Intelligent Transportation System
Qiong Li, Xiangxian Yi, Jin Nie, Yizhao Zhu
Abstract
Opportunistic Federated Learning (OppCL) is a widely adopted distributed learning approach for Intelligent Transportation Systems (ITS). However, the exchange of information among distributed learning parties raises privacy concerns, and OppCL training faces drawbacks such as a high number of iterations, a long training time, and low efficiency. This paper proposes an innovative framework for privacy-preserving reputation-based Opportunistic Federated Learning (PR-OppCL) to address these challenges. Firstly, we introduce a reputation value considering factors like time and model loss. This value guides our incentive mechanism, which encourages high-quality data-seeking clients to join the training process. Secondly, we implement an auction mechanism that allows clients to auction local training tasks to fog nodes, improving efficiency, and addressing performance imbalances. Thirdly, we integrate lightweight cryptographic primitive Oblivious Transfer (OT) into gradient sharing to protect vehicle privacy. Finally, the experiments demonstrate a significant reduction in training rounds and total time, outperforming existing methods.