This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …

Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …

Most commonly used distributed machine learning systems are either synchronous or
centralized asynchronous. Synchronous algorithms like AllReduce-SGD perform poorly in a …

Consensus optimization has received considerable attention in recent years. A number of
decentralized algorithms have been proposed for convex consensus optimization. However …

The topic of stochastic approximation (SA) and its pioneer algorithm (the Robbins-Monro
(RM) algorithm) with methods for its convergence analysis are described. Algorithms …

We study distributed non-convex optimization on a time-varying multi-agent network. Each
node has access to its own smooth local cost function, and the collective goal is to minimize …

This paper considers a problem of distributed hypothesis testing over a network. Individual
nodes in a network receive noisy local (private) observations whose distribution is …

In the era of big data, it is challenging to train a machine learning model on a single machine
or over a distributed system with a central controller over a large-scale dataset. In this paper …

Many modern large-scale machine learning problems benefit from decentralized and
stochastic optimization. Recent works have shown that utilizing both decentralized …