Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows for Multi-Agent Optimization

This research paper introduces a novel class of spatio-temporal compressors for reducing communication bandwidth in distributed optimization algorithms, specifically focusing on prime-dual flows, and proves their effectiveness in achieving asymptotic and exponential convergence for both convex and strongly convex cost functions.