Empirical validation of federated learning with YOLO v7-tiny for road sign detection: A simulation-based comparative study

Abstract

Detecting road signs is a critical component in the development of intelligent driving systems. While centralized machine learning approaches have demonstrated potential in this field, the untapped potential of Federated Learning warrants exploration. This research aims to bridge this gap by examining the feasibility of applying Federated Learning within edge Artificial Intelligence (AI) computing environments for the purpose of road sign detection. Utilizing the You Only Look Once (YOLO) v7-tiny model and a range of experimental parameters demonstrates that Federated Learning is viable and outperforms centralized approaches under specific conditions. The study's empirical analysis highlights the sensitivity of detection accuracy to varying experimental parameters. The study contributes to the existing literature by establishing the efficacy of Federated Learning in road sign detection, particularly in edge AI settings constrained by hardware limitations and privacy concerns. However, the study acknowledges limitations, including the lack of deployment on actual edge AI devices and a restricted range of experimental parameters. Future research should aim for more exhaustive experiments with broader datasets, diverse parameters, and real-world edge AI environments. These findings offer valuable insights for future implementations in intelligent automotive systems.