Federated Learning for Robotic and Autonomous Systems: A Survey on Architectures, Synergies with Distributed Ledger Technologies, and Future Directions

Abstract

The rapid proliferation of autonomous robotic systems, ranging from nano-drones to industrial collaborative robots (cobots), is generating massive, distributed datasets. While deep learning (DL) serves as the cornerstone of modern robotic intelligence, the conventional approach of centralizing this data for training poses insurmountable challenges related to privacy, security, bandwidth, and latency. Federated Learning (FL) has emerged as a disruptive paradigm that enables collaborative model training across distributed devices without the need for raw data exchange. However, the integration of FL into real-world robotic swarms—characterized by extreme heterogeneity, dynamic connectivity, and stringent resource constraints—introduces a unique set of complexities that extend far beyond those of conventional edge devices. This survey provides a comprehensive and critical examination of the burgeoning field of FL within robotic and autonomous systems. We move beyond a mere overview to present a novel taxonomy that classifies FL architectures for robotics based on communication topology, learning paradigm, and application criticality. A significant portion of our analysis is dedicated to the potent synergy between FL and Distributed Ledger Technologies (DLTs), particularly blockchain, for achieving decentralized trust, auditability, and robust aggregation in the presence of potentially malicious agents. We extensively review applications across perception, control, and collaborative tasks, highlighting pioneering works in multi-robot SLAM, federated reinforcement learning, and human-robot interaction. Furthermore, we identify and discuss pressing open challenges, including communication efficiency in mobile swarms, energy-aware client selection, personalized learning for non-IID data, and defense mechanisms against sophisticated adversarial attacks. This paper serves as a foundational reference for researchers and practitioners aiming to develop the next generation of private, secure, and collectively intelligent robotic systems.