[Paper Review] An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems.

Xianjia Yu, Jorge Peña Queralta+2 more|arXiv (Cornell University)|Apr 20, 2021

Privacy-Preserving Technologies in Data90 references4 citations

TL;DR

This paper proposes a hybrid framework integrating Federated Learning (FL) and Distributed Ledger Technologies (DLT) to enhance privacy, security, and robustness in robotic and autonomous systems at the edge. By enabling decentralized model training on isolated data while ensuring integrity and auditability via DLT, the approach supports real-time, privacy-preserving intelligence in distributed autonomous environments.

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.

Motivation & Objective

• Address growing concerns about data security and user privacy in autonomous robotic systems driven by deep learning and edge computing.
• Examine the limitations of federated learning alone in providing sufficient security and robustness for real-world autonomous systems.
• Investigate the synergistic integration of federated learning and distributed ledger technologies (DLT) to enhance trust, integrity, and decentralization in edge AI.
• Provide a comprehensive overview of current research trends, challenges, and applications of FL and DLT in robotic and autonomous systems.
• Establish foundational concepts and key considerations for designing secure, privacy-preserving, and scalable AI systems at the edge.

Proposed method

• Leverage federated learning to train machine learning models on decentralized, local data at the edge without sharing raw data.
• Integrate distributed ledger technologies (DLT) to record and verify model updates, ensuring transparency, immutability, and auditability of the training process.
• Use DLT to maintain a tamper-proof log of model updates and client participation, enhancing system accountability and resilience against adversarial manipulation.
• Combine the privacy-preserving nature of FL with the security and decentralization features of DLT to create a robust framework for autonomous systems.
• Analyze the architectural and operational trade-offs between FL and DLT in edge environments, focusing on latency, bandwidth, and consistency.
• Survey existing applications and research efforts integrating FL and DLT in robotic and autonomous systems, identifying key design patterns and open challenges.

Experimental results

Research questions

• RQ1 How can federated learning be effectively combined with distributed ledger technologies to enhance privacy and security in edge-based autonomous systems?
• RQ2 What are the key architectural and operational challenges in deploying FL and DLT together in real-time robotic applications?
• RQ3 In what ways does DLT improve the trustworthiness and integrity of federated learning processes in decentralized robotic systems?
• RQ4 How do the integration of FL and DLT impact system performance, including latency, communication overhead, and scalability in edge environments?
• RQ5 What are the current research trends and open issues in applying FL and DLT to robotic and autonomous systems?

Key findings

• Federated learning alone is insufficient for securing autonomous systems due to vulnerabilities in model update integrity and lack of accountability.
• Distributed ledger technologies significantly enhance the trust and auditability of federated learning by providing a tamper-resistant record of model updates and client participation.
• The integration of FL and DLT enables decentralized, privacy-preserving AI training while ensuring system resilience against malicious updates and data poisoning.
• The combined framework supports real-time collaboration among distributed robotic agents without exposing raw data, aligning with privacy-by-design principles.
• Current research shows that the integration of FL and DLT introduces manageable overheads in communication and storage, but these are acceptable for high-assurance autonomous applications.
• The survey identifies a growing body of work exploring hybrid FL-DLT architectures, particularly in smart cities, industrial automation, and autonomous vehicles, highlighting practical deployment potential.