The robustness of distributed optimization is an emerging field of study, motivated by various applications of distributed optimization including distributed machine learning, distributed sensing, and swarm robotics. With the rapid expansion of the scale of distributed systems, resilient distributed algorithms for optimization are needed, in order to mitigate system failures, communication issues, or even malicious attacks. This survey investigates the current state of fault-tolerance research in distributed optimization, and aims to provide an overview of the existing studies on both fault-tolerant distributed optimization theories and applicable algorithms.

中文翻译：

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分布式优化和机器学习中的容错性调查

分布式优化的鲁棒性是一个新兴的研究领域，受到分布式优化的各种应用的推动，包括分布式机器学习、分布式传感和群体机器人。随着分布式系统规模的迅速扩大，需要有弹性的分布式优化算法，以缓解系统故障、通信问题甚至恶意攻击。本次调查调查了分布式优化中容错研究的现状，旨在提供对容错分布式优化理论和适用算法的现有研究的概述。