CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV

Nowadays, vehicles can provide many valuable data (such as the videos recorded by dashcams) for analytical model building. Integrating vehicular ad hoc networks with the Internet of Things (IoT), the Internet of Vehicles (IoV) has a promising future. In IoV, vehicles maintain their own communication, computing, and learning capabilities. Thus, instead of sending the data to a central server for model training, which leads to a high communication overhead, vehicles can train the data locally. However, it is still a challenge to preserve the privacy while keeping both the communication and computation overheads of vehicles acceptable. In this article, we present a distributed machine learning framework with a two-layered architecture. The architecture uniquely involves vehicle clusters, roadside units, and a central server, which provides a basic guarantee to the vehicle privacy and also limits the overhead. By carefully adopting cryptographic tools and techniques, the framework has the following properties: 1) it preserves the privacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the application layer; 4) the evaluation shows that it is lightweight for vehicles. Compared with other existing works, the proposed framework is more suitable for IoV.