A4 Artikkeli konferenssijulkaisussa

Federated Learning in Robotic and Autonomous Systems

Julkaisun tekijät: Yu Xianjia, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi

Konferenssin vakiintunut nimi: International Conference on Mobile Systems and Pervasive Computing

Kustantaja: Elsevier B.V.

Julkaisuvuosi: 2021

Journal: Procedia Computer Science

Kirjan nimi *: 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

Tietokannassa oleva lehden nimi: Procedia Computer Science

Sarjan nimi: Procedia Computer Science

Volyymi: 191

ISSN: 1877-0509

DOI: http://dx.doi.org/10.1016/j.procs.2021.07.041


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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Last updated on 2021-15-10 at 10:22