论文标题
分布式的鞍点问题：下限，近乎最佳和鲁棒算法
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
论文作者
论文摘要
本文着重于随机鞍点问题的分布式优化。本文的第一部分专门针对平滑（强）凸的（强）凹形鞍点问题的集中式和分散的分布式方法，以及实现这些边界的近乎最佳的算法。接下来，我们提出了一种用于集中分布式鞍点问题的新联合算法 - 额外的步骤本地SGD。对新方法进行的理论分析是针对强烈凸出的凹形和非convex-non-concave问题的。在本文的实验部分中，我们在实践中显示了我们方法的有效性。特别是，我们以分布方式训练甘恩。
This paper focuses on the distributed optimization of stochastic saddle point problems. The first part of the paper is devoted to lower bounds for the centralized and decentralized distributed methods for smooth (strongly) convex-(strongly) concave saddle point problems, as well as the near-optimal algorithms by which these bounds are achieved. Next, we present a new federated algorithm for centralized distributed saddle-point problems - Extra Step Local SGD. The theoretical analysis of the new method is carried out for strongly convex-strongly concave and non-convex-non-concave problems. In the experimental part of the paper, we show the effectiveness of our method in practice. In particular, we train GANs in a distributed manner.
