An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems

TL;DR

This paper surveys how federated learning and distributed ledger technologies enhance privacy, security, and autonomy in robotic and autonomous systems, emphasizing their integration and current research challenges.

Contribution

It provides a comprehensive overview of federated learning applications in autonomous robots and analyzes the role of blockchain and DLTs in enhancing system security.

Findings

Federated learning enables privacy-preserving AI at the edge.

Distributed ledger technologies improve security and trust.

Integration of FL and DLTs addresses security concerns in autonomous systems.

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.