instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
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.
answer:
we find 99.9% of the gradient exchange in distributed SGD is redundant; we reduce the communication bandwidth by two orders of magnitude without losing accuracy. 


question:
Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed. However, the value of pruning has largely evaded scrutiny. In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations. First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network. Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable. Such architectures are powerful when trained from scratch. Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.
answer:
Training small networks beats pruning, but pruning finds good small networks to train that are easy to copy.


question:
Computer simulation provides an automatic and safe way for training robotic control
 policies to achieve complex tasks such as locomotion. However, a policy
 trained in simulation usually does not transfer directly to the real hardware due
 to the differences between the two environments. Transfer learning using domain
 randomization is a promising approach, but it usually assumes that the target environment
 is close to the distribution of the training environments, thus relying
 heavily on accurate system identification. In this paper, we present a different
 approach that leverages domain randomization for transferring control policies to
 unknown environments. The key idea that, instead of learning a single policy in
 the simulation, we simultaneously learn a family of policies that exhibit different
 behaviors. When tested in the target environment, we directly search for the best
 policy in the family based on the task performance, without the need to identify
 the dynamic parameters. We evaluate our method on five simulated robotic control
 problems with different discrepancies in the training and testing environment
 and demonstrate that our method can overcome larger modeling errors compared
 to training a robust policy or an adaptive policy.
answer:

We propose a policy transfer algorithm that can overcome large and challenging discrepancies in the system dynamics such as latency, actuator modeling error, etc.