Abstract
Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.
Highlights
Internet of Vehicles (IoV) is a new type of industry with deep integration of automobile, electronics, information communication, transportation, and traffic management
With the same data scale, the success rates of two tests based on federated learning pool (FLP) are higher than the traditional distributed machine learning algorithm based on the logistic regression model, which means our model is better in terms of accuracy
We innovatively propose a blockchain-based federated learning pool framework for data silos and data privacy disclosure in IoV and design a lightweight encryption algorithm called CPC to combine with it
Summary
Internet of Vehicles (IoV) is a new type of industry with deep integration of automobile, electronics, information communication, transportation, and traffic management. In vehicle-tovehicle (V2V) communications [1], which is one of the IoT applications, the functions running on our sensor nodes can be part of the on-board system of each vehicle and the functions running on base stations can be running on roadside devices In this way, the local transportation department can accurately and comprehensively grasp the realtime traffic information to conduct intelligent analysis and make decisions. To solve a series of security problems in IoV environment, we propose a blockchain-based federated learning pool (BFLP) framework and integrate a lightweight encryption algorithm into it. In this framework, we combine blockchain and federated learning to protect user’s data privacy.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
