Binary Encoding-Based Federated Learning for Traffic Sign Recognition in Autonomous Driving

Yian Wen, Yun Zhou () and Kai Gao
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Yian Wen: School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
Yun Zhou: School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
Kai Gao: College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China

Mathematics, 2024, vol. 12, issue 14, 1-13

Abstract: Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is urgent to introduce a distributed machine learning approach to protect private data of connected vehicles. In this paper, we propose a local differential privacy-based binary encoding federated learning approach. The binary encoding techniques and random perturbation methods are used in distributed learning scenarios to enhance the efficiency and security of data transmission. For the vehicle layer in this approach, the model is trained locally, and the model parameters are uploaded to the central server through encoding and perturbing. The central server designs the corresponding decoding, correction scheme, and regression statistical method for the received binary string. Then, the model parameters are aggregated and updated in the server and transmitted to the vehicle until the learning model is trained. The performance of the proposed approach is verified using the German Traffic Sign Recognition Benchmark data set. The simulation results show that the convergence of the approach is better with the increase in the learning cycle. Compared with baseline methods, such as the convolutional neural network, random forest, and backpropagation, the proposed approach achieves higher accuracy in the process of traffic sign recognition, with an increase of 6%.

Keywords: federated learning; local differential privacy; traffic sign recognition; autonomous driving (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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