Efficient Variance-Reduced Learning Over Multi-Agent Networks

This work develops a fully decentralized variance-reduced learning algorithm for multi-agent networks where nodes store and process the data locally and are only allowed to communicate with their immediate neighbors. In the proposed algorithm, there is no need for a central or master unit while the objective is to enable the dispersed nodes to learn the exact global model despite their limited localized interactions. The resulting algorithm is shown to have low memory requirement, guaranteed linear convergence, robustness to failure of links or nodes and scalability to the network size. Moreover, the decentralized nature of the solution makes large-scale machine learning problems more tractable and also scalable since data is stored and processed locally at the nodes.