Decentralized Deep Learning using Momentum-Accelerated Consensus

• URL: http://arxiv.org/abs/2010.11166v2
• Date: Sat, 28 Nov 2020 17:06:17 GMT
• Title: Decentralized Deep Learning using Momentum-Accelerated Consensus
• Authors: Aditya Balu, Zhanhong Jiang, Sin Yong Tan, Chinmay Hedge, Young M Lee, Soumik Sarkar
• Abstract summary: We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. We propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology. Our algorithm is based on the heavy-ball acceleration method used in gradient-based protocol.
• Score: 15.333413663982874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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