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On Accelerating Distributed Convex Optimizations
[article]
2021
arXiv
pre-print
This paper studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associated local cost function. The agents are connected to a server, and there is no inter-agent communication. The agents' goal is to learn a parameter vector that optimizes the aggregate of their local costs without revealing their local data points. In principle, the agents can solve this problem by collaborating with the
arXiv:2108.08670v1
fatcat:mpoxl5udtbdx5hbighhrbbnt2a