Paper:
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches are limited by network bandwidth, we propose the use of communication compression in the decentralized training context. We show that CHOCO-SGD achieves linear speedup in the number of workers for arbitrary high compression ratios on general non-convex functions, and non-IID training data. We demonstrate the practical performance of the algorithm in two key scenarios: the training of deep learning models (i) over decentralized user devices, connected by a peer-to-peer network and (ii) in a datacenter.

Review:
The authors present an algorithm CHOCO-SGD to make use of communication compression in a decentralized setting. This is an interesting problem and the results are promising. Firstly they prove the convergence rate of the algorithm on non-convex smooth functions, which shows a nearly linear speedup. 
Second, on the practical part, there have 3 main results:
	1. They compare CHOCO-SGD under various compression schemes with the baseline. The results show the algorithm generally outperforms the baseline.
	2. They implement it over a realistic peer-to-peer social network and show a great communication performance under such a network with limited bandwidth.
	3. In a datacenter setting, they compare the algorithm with all-reduce, which is a centralized communication method. The results show a strong training reduction for CHOCO-SGD.
Also, the paper is mostly nicely written.
However, there have several issues:
	1. In the introduction, they introduce their experiments with the order from "datacenter experiment" to "peer-to-peer experiment", which is different from the actual presenting order.
	2. In the description of Algorithm 1, the representation of initial values should be x{(-1/2)}_{i} instead of x{(0)}_{i} since line 2 using the term x^{t-1/2}_{i} with the range of t from 0 to T-1.
	3. About "datacenter setting" experiment, it seems not an apple to apple comparison between CHOCO-SGD and all-reduce method since CHOCO-SGD stands for the decentralized algorithm with compression and all-reduce stands for a centralized algorithm without compression. It's better to compare with at least one centralized algorithm with a compression scheme (like QSGD[1], signSGD[2], DGC[3]).
	4. Although they compare with the baseline (DCD and ECD) on Cifar-10 dataset,  it's worth to compare with them on the ImageNet since the result may be different under large-scale training.
Overall, this could be a great paper if fixing the issues above.
[1] D. Alistarh, D. Grubic, J. Z. Li, R. Tomioka, and M. Vojnovic. QSGD: Communication-efﬁcient SGD via gradient quantization and encoding. In Proc. Advances in Neural Information Processing Systems (NIPS), 2017.
[2] Bernstein J, Zhao J, Azizzadenesheli K, Anandkumar A. signSGD with majority vote is communication efficient and fault tolerant. arXiv. 2018 Oct 11.
[3] Lin Y, Han S, Mao H, Wang Y, Dally WJ. Deep gradient compression: Reducing the communication bandwidth for distributed training. arXiv preprint arXiv:1712.01887. 2017 Dec 5.