Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum

Distributed stochastic gradient methods are widely used to preserve data privacy and ensure scalability in large-scale learning tasks. While existing theory on distributed momentum Stochastic Gradient Descent (mSGD) mainly focuses on time-averaged convergence, the more practical last-iterate convergence remains underexplored. In this work, we analyze the last-iterate convergence behavior of distributed mSGD in non-convex settings under the classical Robbins-Monro step-size schedule. We prove both almost sure convergence and L2 convergence of the last iterate, and derive convergence rates. We further show that momentum can accelerate early-stage convergence, and provide experiments to support our theory.