  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.
