Perfect synchronization in distributed machine learning problems is
inefficient and even impossible due to the existence of latency, package losses
and stragglers. We propose a Robust Fully-Asynchronous Stochastic Gradient
Tracking method (R-FAST), where each device performs local computation and
communication at its own pace without any form of synchronization. Different
from existing asynchronous distributed algorithms, R-FAST can eliminate the
impact of data heterogeneity across devices and allow for packet losses by
employing a robust gradient tracking strategy that relies on properly designed
auxiliary variables for tracking and buffering the overall gradient vector.
More importantly, the proposed method utilizes two spanning-tree graphs for
communication so long as both share at least one common root, enabling flexible
designs in communication architectures. We show that R-FAST converges in
expectation to a neighborhood of the optimum with a geometric rate for smooth
and strongly convex objectives; and to a stationary point with a sublinear rate
for general non-convex settings. Extensive experiments demonstrate that R-FAST
runs 1.5-2 times faster than synchronous benchmark algorithms, such as
Ring-AllReduce and D-PSGD, while still achieving comparable accuracy, and
outperforms existing asynchronous SOTA algorithms, such as AD-PSGD and OSGP,
especially in the presence of stragglers.

通过提出一种称为 R-FAST 的稳健全异步随机梯度跟踪方法，该方法可以消除数据异构性、允许数据丢失，并利用两个树形图进行通信，从而在分布式机器学习中实现了高效的非同步算法。

分布式训练通用架构的稳健全异步方法

Robust Fully-Asynchronous Methods for Distributed Training over General  Architecture

Communication could potentially be an effective way for multi-agent
cooperation. However, information sharing among all agents or in predefined
communication architectures that existing methods adopt can be problematic.
When there is a large number of agents, agents cannot differentiate valuable
information that helps cooperative decision making from globally shared
information. Therefore, communication barely helps, and could even impair the
learning of multi-agent cooperation. Predefined communication architectures, on
the other hand, restrict communication among agents and thus restrain potential
cooperation. To tackle these difficulties, in this paper, we propose an
attentional communication model that learns when communication is needed and
how to integrate shared information for cooperative decision making. Our model
leads to efficient and effective communication for large-scale multi-agent
cooperation. Empirically, we show the strength of our model in a variety of
cooperative scenarios, where agents are able to develop more coordinated and
sophisticated strategies than existing methods.

本文提出了一种注意力通信模型，能够学习何时需要进行通信以及如何将共享信息集成到合作决策中，以实现大规模多智能体协作的高效和有效通信，从而在合作情境中比现有方法展现出更具协调性和更复杂的策略。