CDMSGD) algorithm for collaborative deep learning over fixed topology networks that enables data parallelization as well as decentralized computation.

Neural Information Processing Systems http://nips.cc/

Large Batch Size had till recently been viewed as a deterrent for good accuracy. However recent studies show that increasing the batch size can significantly reduce the training time while maintaining a considerable level of accuracy. In this blog, we draw on our inferences from four such technical papers. The RMSprop Warm-up phase is used to address the optimization difficulty at the start of the training. The update rule demonstrated below utilizes both the Stochastic Gradient Descent (SGD) along the RMSprop optimization algorithm.

There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model parallelization and engaging multiple computing agents via using a central parameter server, aspect of data parallelization along with decentralized computation has not been explored sufficiently. In this context, this paper presents a new consensus-based distributed SGD (CDSGD) (and its momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed topology networks that enables data parallelization as well as decentralized computation. Such a framework can be extremely useful for learning agents with access to only local/private data in a communication constrained environment. We analyze the convergence properties of the proposed algorithm with strongly convex and nonconvex objective functions with fixed and diminishing step sizes using concepts of Lyapunov function construction. We demonstrate the efficacy of our algorithms in comparison with the baseline centralized SGD and the recently proposed federated averaging algorithm (that also enables data parallelism) based on benchmark datasets such as MNIST, CIFAR-10 and CIFAR-100.