A4 Refereed article in a conference publication
Federated Learning in Robotic and Autonomous Systems
Authors: Yu Xianjia, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi
Editors: Elhadi Shakshuki, Ansar Yasar
Conference name: International Conference on Mobile Systems and Pervasive Computing
Publisher: Elsevier B.V.
Publication year: 2021
Journal: Procedia Computer Science
Book title : The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), The 16th International Conference on Future Networks and Communications (FNC), The 11th International Conference on Sustainable Energy Information Technology
Journal name in source: Procedia Computer Science
Series title: Procedia Computer Science
Volume: 191
First page : 135
Last page: 142
ISSN: 1877-0509
DOI: https://doi.org/10.1016/j.procs.2021.07.041
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/67416232
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.
Downloadable publication This is an electronic reprint of the original article. |