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https://pubmed.ncbi.nlm.nih.gov/38117628
The study proposes a distributed subgradient method with random quantization and flexible weights, providing convergence analysis for convex and weakly convex objective functions in the context of large-scale distributed optimization problems, addressing communication privacy and quality issues.