Federated Learning for Object Detection in Autonomous Vehicles
Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021
DOI
10.1109/BigDataService52369.2021.00018
First Page
107
Last Page
114
Abstract
With the recent proliferation of Artificial Intelligence (AI), object detection is becoming increasingly ubiquitous. It is one of the key features of Autonomous Driving Systems. In current applications, object detection models are usually trained at a centralized location by collecting data from multiple sources. This raises concerns about data privacy among other issues. This paper addresses data privacy through a Federated Learning (FL) approach. FL architecture aims at preserving data privacy while maintaining performance by training the model in a decentralized manner. In this paper, we analyze how FL impacts the performance of object detection in a real-world traffic environment. We have constructed a prototype FL system and evaluated it on the KITTI Vision Benchmark 2D image dataset. In our prototype, object detection models are trained locally on a vehicle's dataset, and the resultant weights are securely aggregated, using symmetric encryption techniques during data transfer, at the global server to yield an improved model. The FL model converged at 68% mean average precision. We compared the performance of object detection using FL to the traditional deep learning approach and noticed significant difference between the two models.
Department
Computer Engineering
Recommended Citation
Deepthi Jallepalli, Navya Chennagiri Ravikumar, Poojitha Vurtur Badarinath, Shravya Uchil, and Mahima Agumbe Suresh. "Federated Learning for Object Detection in Autonomous Vehicles" Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021 (2021): 107-114. https://doi.org/10.1109/BigDataService52369.2021.00018
