Large Model-assisted Federated Learning for Object Detection of Autonomous Vehicles in Edge

The advancement of Autonomous Vehicles (AVs) significantly relies on the integration of Internet-of-Things technology for real-time data processing and decision-making. Object detection, a critical component of AVs, necessitates utilizing machine learning techniques. However, this poses challenges including data privacy and the inefficiency of transmitting large volumes of data to a central server. To overcome these challenges, we employ Federated Learning (FL), which allows local devices to train a global model without sharing their raw data. While effective, synchronous federated learning can be time-consuming due to stragglers and often encounter device dropout issues. In contrast, asynchronous federated learning provides faster updates but frequently yields sub-optimal models compared to its synchronous counterpart. Motivated by that, in this paper, we introduce a novel FL framework that combines the strengths of both synchronous and asynchronous methods. Further, by organizing devices into a hierarchical structure, we aim to optimize model convergence while mitigating straggler and dropout problems. Moreover, deploying large models on local edge devices is impractical due to limited computational capabilities, necessitating the adoption of lightweight models to mitigate prolonged training and prediction duration. To evaluate the performance of the proposed method, we conduct a series of experiments using publicly available datasets and subsequently compare the results. Our findings demonstrate that the proposed method significantly enhances the model convergence and performance in object detection for AVs.