Lossy gradient compression has become a practical tool to overcome the
communication bottleneck in centrally coordinated distributed training of
machine learning models. However, algorithms for decentralized training with
compressed communication over arbitrary connected networks have been more
complicated, requiring additional memory and hyperparameters. We introduce a
simple algorithm that directly compresses the model differences between
neighboring workers using low-rank linear compressors applied on model
differences. Inspired by the PowerSGD algorithm for centralized deep learning,
this algorithm uses power iteration steps to maximize the information
transferred per bit. We prove that our method requires no additional
hyperparameters, converges faster than prior methods, and is asymptotically
independent of both the network and the compression. Out of the box, these
compressors perform on par with state-of-the-art tuned compression algorithms
in a series of deep learning benchmarks.

介绍了一种使用低秩线性压缩器直接压缩相邻工作器之间的模型差异的简单算法，在分布式机器学习训练中具有实际应用价值，无需额外的超参数，收敛速度比之前的方法快，并且在一系列深度学习基准测试中的表现与经过调优的最先进压缩算法相当。