Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning for Autonomous Visual Robot Navigation

Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single...

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Bibliographic Details

Published in:	IEEE robotics and automation letters Vol. 9; no. 12; pp. 11841 - 11848
Main Authors:	Gummadi, Shreya, Gasparino, Mateus V., Vasisht, Deepak, Chowdhary, Girish
Format:	Journal Article
Language:	English
Published:	IEEE 01.12.2024
Subjects:	Bandwidth Computational modeling Computer vision Data models Decentralized control Distributed robot systems Federated learning Navigation Predictive models Robot vision systems robotics in under-resourced settings Servers Training vision-based navigation
ISSN:	2377-3766, 2377-3766
Online Access:	Get full text
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