边缘联邦学习和机器人和自治系统的分布式账本技术概述
An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems
摘要：
随着计算和通信解决方案的进步，自治系统变得无处不在，从而实现分布式设备的低延迟卸载和实时协作。区块链和分布式账本技术（DLT）的去中心化技术正在发挥关键作用。与此同时，深度学习（DL）的进步显著提高了机器人和自主系统的自主程度和智能水平。在这些技术革命发生的同时，对数据安全和最终用户隐私的担忧已成为不可避免的研究考虑因素。联邦学习（FL）是边缘隐私保护DL的一种有前途的解决方案，通过在孤立的数据岛上学习和仅通信模型更新，具有固有的分布式性质。然而，FL本身并不提供当今分布式自治系统标准所要求的安全性和鲁棒性水平。本调查涵盖了FL在自主机器人中的应用，分析了DLT和FL在这些系统中的作用，并介绍了当前研究中的关键背景概念和考虑因素。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
