Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
This addresses the communication cost problem for distributed training of large-scale machine learning models, but it is incremental as it adapts existing compression techniques to an adaptive gradient method.
The paper tackles the communication bottleneck in distributed nonconvex optimization by proposing a communication-compressed AMSGrad method, which converges with the same iteration complexity as uncompressed AMSGrad while reducing communication costs, as supported by experiments on benchmarks.
Due to the explosion in the size of the training datasets, distributed learning has received growing interest in recent years. One of the major bottlenecks is the large communication cost between the central server and the local workers. While error feedback compression has been proven to be successful in reducing communication costs with stochastic gradient descent (SGD), there are much fewer attempts in building communication-efficient adaptive gradient methods with provable guarantees, which are widely used in training large-scale machine learning models. In this paper, we propose a new communication-compressed AMSGrad for distributed nonconvex optimization problem, which is provably efficient. Our proposed distributed learning framework features an effective gradient compression strategy and a worker-side model update design. We prove that the proposed communication-efficient distributed adaptive gradient method converges to the first-order stationary point with the same iteration complexity as uncompressed vanilla AMSGrad in the stochastic nonconvex optimization setting. Experiments on various benchmarks back up our theory.