Communication Efficient Framework for Decentralized Machine Learning

In this paper, we propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The key novelty in the proposed algorithm is that it solves the problem in a decentralized topology where at most half of the workers are competing the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets.