research-article

Federated Learning in Robotic and Autonomous Systems

Authors:

Jorge Peña Queralta,

Jukka Heikkonen,

Tomi WesterlundAuthors Info & Claims

Volume 191, Issue C

Pages 135 - 142

https://doi.org/10.1016/j.procs.2021.07.041

Published: 01 January 2021 Publication History

Abstract

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.

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Cited By

Dong JLi HCong YSun GZhang YVan Gool L(2024)No One Left Behind: Real-World Federated Class-Incremental LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.333421346:4(2054-2070)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TPAMI.2023.3334213
Marino RCarnevale LFazio MVillari MFox GRana OFazio MVillari M(2023)Make Federated Learning a Standard in Robotics by Using ROS2Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies10.1145/3632366.3632373(1-6)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3632366.3632373
Yang PJiang YWen DWang TJones CShi Y(2023)Decentralized Over-the-Air Federated Learning by Second-Order Optimization MethodIEEE Transactions on Wireless Communications10.1109/TWC.2023.332761023:6(5632-5647)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3327610

Index Terms

Federated Learning in Robotic and Autonomous Systems

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 191, Issue C

2021

541 pages

ISSN:1877-0509

EISSN:1877-0509

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Copyright © 2021.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2021

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Cited By

Dong JLi HCong YSun GZhang YVan Gool L(2024)No One Left Behind: Real-World Federated Class-Incremental LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.333421346:4(2054-2070)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TPAMI.2023.3334213
Marino RCarnevale LFazio MVillari MFox GRana OFazio MVillari M(2023)Make Federated Learning a Standard in Robotics by Using ROS2Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies10.1145/3632366.3632373(1-6)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3632366.3632373
Yang PJiang YWen DWang TJones CShi Y(2023)Decentralized Over-the-Air Federated Learning by Second-Order Optimization MethodIEEE Transactions on Wireless Communications10.1109/TWC.2023.332761023:6(5632-5647)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3327610

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