Homomorphic federated learning schemes enabled pedestrian and vehicle detection system

Abstract

Intelligent transport systems are increasingly being used in practice these days. Fog nodes and cloud servers collect real-time pedestrian and vehicle data and train them based on machine learning models. Existing pedestrian and vehicle detection systems need more security, less resource leakage, and faster processing. This paper proposes a homomorphic, secure, federated learning-enabled pedestrian detection system named HMFLS. The HMFLS consists of base stations (BS) and homogeneous (homo) federated learning servers with weights, surveillance, and traffic light components, and these nodes serve as data generation and processing sources. Homomorphic encryption is a cryptographic technique that allows computations on encrypted data without decryption. In other words, it enables computations on encrypted data while preserving the privacy and confidentiality of the information. The HMFLS exploits Generative Adversarial Networks to train pedestrian and vehicle images and extract features based on VGG19 from fog nodes and surveillance sensor images. We trained the different tasks and weights based on the model’s 28,000 pedestrian and vehicle images. The goal is to identify pedestrians and vehicles in the system. The interface is based on an Android-based application that can be easily integrated into different vehicles and mobile phones. Simulation results demonstrated that HMFL performed well as compared to existing schemes (e.g., PEL, TFL-CNN, FLAV) in terms of security accuracy by 98%, resource leakage by 50%, and processing time by 52% for vehicle and pedestrian detection in the system.