Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization
Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization
This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and …