Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization. (arXiv:2205.11979v1 [math.OC])

In recent years, decentralized learning has emerged as a powerful tool not
only for large-scale machine learning, but also for preserving privacy. One of
the key challenges in decentralized learning is that the data distribution held
by each node is statistically heterogeneous. To address this challenge, the
primal-dual algorithm called the Edge-Consensus Learning (ECL) was proposed and
was experimentally shown to be robust to the heterogeneity of data
distributions. However, the convergence rate of the ECL is provided only when …

algorithm analysis arxiv decentralized math optimization stochastic