Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
This addresses the scalability and cost issues of distributed training, particularly for federated learning on mobile devices, by enabling training on low-bandwidth networks.
The paper tackles the problem of high communication bandwidth required for gradient exchange in distributed training, proposing Deep Gradient Compression (DGC) which achieves compression ratios from 270x to 600x without accuracy loss, reducing gradient sizes from 97MB to 0.35MB for ResNet-50 and from 488MB to 0.74MB for DeepSpeech.
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile. Code is available at: https://github.com/synxlin/deep-gradient-compression.