Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange
This work investigates multi-token RW learning under two information exchange policies, which average stochastic gradients and updated models, respectively, when tokens meet, and establishes iteration complexity lower bounds for non-convex, convex, and strongly convex objectives on complete graphs.