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A Survey on Fault-tolerance in Distributed Optimization and Machine Learning
[article]
2021
arXiv
pre-print
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
arXiv:2106.08545v2
fatcat:g6fys4icrbbr5k3bd3ycylaptu