  We consider the problem of decentralized deep learning where multiple agents
collaborate to learn from a distributed dataset. While there exist several
decentralized deep learning approaches, the majority consider a central
parameter-server topology for aggregating the model parameters from the agents.
However, such a topology may be inapplicable in networked systems such as
ad-hoc mobile networks, field robotics, and power network systems where direct
communication with the central parameter server may be inefficient. In this
context, we propose and analyze a novel decentralized deep learning algorithm
where the agents interact over a fixed communication topology (without a
central server). Our algorithm is based on the heavy-ball acceleration method
used in gradient-based optimization. We propose a novel consensus protocol
where each agent shares with its neighbors its model parameters as well as
gradient-momentum values during the optimization process. We consider both
strongly convex and non-convex objective functions and theoretically analyze
our algorithm's performance. We present several empirical comparisons with
competing decentralized learning methods to demonstrate the efficacy of our
approach under different communication topologies.
