A Collaborative Real-Time Object Detection and Data Association Framework for Autonomous Robots Using Federated Graph Neural Network
Pages 280 - 288
Abstract
Autonomous robotics require secure and decentralized decision-making systems that ensure data privacy and computational efficiency, especially in critical areas. Current centralized models or human input are associated with data breaches and security vulnerabilities. To counter these, we propose CoRODDA, a dedicated framework combining federated learning and graph neural networks. CoRODDA enhances object detection and data association in autonomous robots, enabling them to learn from local data while preserving privacy and interpreting graph-structured associated data to understand the surrounding environments. The experiments showed the effectiveness of CoRODDA compared to the state-of-the-art, particularly in non-detected objects, improving data privacy and decision-making capabilities.
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Published In
Dec 2023
293 pages
ISBN:978-3-031-61230-5
DOI:10.1007/978-3-031-61231-2
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 16 June 2024
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