Active Federated YOLOR Model for Enhancing Autonomous Vehicles Safety

Gaith Rjoub, Jamal Bentahar, Y. A. Joarder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


A precise and real-time object detection system is crucial to ensuring the safety, smoothness, and trust of Autonomous Vehicles (AVs). Several machine learning techniques have been designed to improve vehicle detection capabilities and reduce the shortcomings caused by limited data and by transferring these data to a central server, which has shown poor performance under different conditions. In this paper, we propose an active federated learning-integrated solution over AVs that capitalizes on the You Only Learn One Representation (YOLOR) approach, a Convolutional Neural Network (CNN) specifically designed for real-time object detection. Our approach combines implicit and explicit knowledge, together with active learning and federated learning with the aim of improving the detection accuracy. Experiments show that our solution achieves better performance than traditional solutions (i.e., Gossip decentralized model and Centralized model).

Original languageBritish English
Title of host publicationMobile Web and Intelligent Information Systems - 18th International Conference, MobiWIS 2022, Proceedings
EditorsIrfan Awan, Muhammad Younas, Aneta Poniszewska-Marańda
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031143908
StatePublished - 2022
Event18th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2022 - Virtual, Online
Duration: 22 Aug 202224 Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13475 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2022
CityVirtual, Online


  • Active Learning
  • Autonomous vehicles
  • Edge computing
  • Federated Learning
  • Object detection


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