GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange
This addresses the need for efficient deep learning training in distributed systems, though it appears incremental as it builds on existing gossip and SGD techniques.
The paper tackles the problem of speeding up convolutional neural network training by proposing GoSGD, a distributed optimization method based on gossip algorithms, which achieves faster convergence in experiments.
We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way of sharing information between different threads based on gossip algorithms that show good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized.