Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent serverto-client; and (2) Federated Dropout, which allows users to efficiently train locally on smaller subsets of the global model and also provides a reduction in both client-to-server communication and local computation. We empirically show that these strategies, combined with existing compression approaches for client-toserver communication, collectively provide up to a 14× reduction in server-toclient communication, a 1.7× reduction in local computation, and a 28× reduction in upload communication, all without degrading the quality of the final model. We thus comprehensively reduce FL's impact on client device resources, allowing higher capacity models to be trained, and a more diverse set of users to be reached.
The paper presents some new approaches for communication efficient Federated Learning (FL) that allows for training of large models on heterogeneous edge devices. In FL, heterogeneous edge devices have access to potentially non-iid samples of data points and try to jointly learn a model by averaging their local models at a parameter server (the cloud). As the bandwidth of the up/downlink-link may be limited communication overheads may become the bottleneck during FL. Moreover, due to the heterogeneity of the hardware, large models may be hard to train on small devices. Due to that, there are several recent approaches that aim to minimize communication via methods of quantization, which also aim to allow for smaller models via methods of compression and model quantization.
In this paper, the authors suggest a combination of two methods to reduce communication and allow for large model training by 1) using a lossy compressed model when that is communicated from the cloud to the edge devices, and 2) subsampling the gradients, a form of dropout, at the edge device side that allows for an overall smaller model update. The novelty of either of those techniques is quite limited as individually they have been suggested before, but the combination of both of them is interesting. 
The paper is overall well written, however there are two aspects that make the contribution lacking in novelty. First of all, the presented methods are a combination of existing techniques, that although interesting to combine together, are neither theoretically analyzed nor extensively tested. The model/update quantization technique has been used in the past extensively [eg 1-3]. Then, the “federated dropout” can be seen as a “coordinate descent” type of a technique, i.e., randomly zeroing out gradient elements per iteration. 
Since this is a more experimental paper, the setup tested is quite limited in its comparisons. For example, one would expect to see extensive comparisons with methods for quantizing gradients, eg QSGD, or Terngrad, and combinations of that with DeepCompression. Although the authors do make an effort to experiment with a different set of hyperparameters (dropout probability, quantization levels, etc), a comparison with state of the art methods is lacking.
Overall, although the combination of the presented ideas has some merit, the lack of extensive experiments that would compare it with the state of the art is not convincing, and the overall effectiveness of this method is unclear at this point.
[1] https://arxiv.org/pdf/1510.00149.pdf
[2] https://arxiv.org/pdf/1803.03383.pdf
[4] https://arxiv.org/pdf/1610.05492.pdf