To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data-of representative samples, of outliers, of misclassifications-is an essential tool in a) identifying and fixing problems in the data, b) generating new modeling hypotheses, and c) assigning or refining human-provided labels. However, manual data inspection is problematic for privacy-sensitive datasets, such as those representing the behavior of realworld individuals. Furthermore, manual data inspection is impossible in the increasingly important setting of federated learning, where raw examples are stored at the edge and the modeler may only access aggregated outputs such as metrics or model parameters. This paper demonstrates that generative models-trained using federated methods and with formal differential privacy guarantees-can be used effectively to debug many commonly occurring data issues even when the data cannot be directly inspected. We explore these methods in applications to text with differentially private federated RNNs and to images using a novel algorithm for differentially private federated GANs.
Goals
The paper identifies a key challenge in a large class of real world federate learning problems where we also have to ensure user level data privacy. In these settings the modeler can not inspect the raw data samples from the user (due to privacy concerns) and hence all modeling tasks (from data wrangling to hypothesis generation to labeling to model class selection to validation) become far more challenging. The paper proposes that in these circumstances one may use a generative model that learns the data distribution using federated learning methods with provable differentiable privacy guarantees. The generative model can then produce data (unconditional, or conditional on some features or class labels) which can be inspected by the modeler without compromising user privacy. 
Experiments
The authors illustrate the approach using existing federated DP RNN learning methods, and using a slightly novel GAN learning algorithm for images (largely similar to other algorithms). They use these methods to provide two examples: 1) learning a language model from text (word sequences) where there is a bug in pre-processing steps (tokenization); 2) learning a GAN for images of handwriting on checks where there is a pre-processing bug that inverts the grayscale of images. These examples illustrate the potential for such methods to possibly be useful to modelers. While one may quibble about some details (see section below) the experimental set up is reasonable to illustrate the need and some of the challenges modelers are likely to face in the real world (
Evaluation & Questions
I'm really torn because I really enjoyed the paper very much overall but I have some strong concerns as well. 
Positives: the paper is well motivated and very well written (it is really a pleasure to read, and it is very clear about the details -- especially after they release the code it should be possible to reproduce the results too). The authors shine a spot light on a problem that is very important & widespread (eg while learning from condifential data on cell phones). The proposed solution is fairly simple, intuitive, and quite high level (lets use a generative model that creates phantom data that can be inspected)
Negatives: I am not entirely sold on this being a realistic approach in the long term -- ie that some of the key problems will ever be solvable (I'm quite ok even if they are not solved now in the first paperr). The authors do a very good job of being transparent about several potential issues (see eg last paragraphs of main paper and appendix D). My biggest concerns are below:
* the phantom samples generated from the model need to be very realistic in order to be useful. In other words, we need to have excellent, high fidelity generate models. Even to create proper hypothesis, create proper model classes, assess convergence, or assess whether the generative model is good enough one needs to be able to inspect the raw data -- which can not be done in the first place. This can not be entirely automated eliminating need for human inspection -- and the problem is much worse in generative models (which need to encode more information) than in discriminative models which need to encode less information (bits) almost by definition. Thus one has simply traded the problem of needing to inspect data to model the final algorithm (whcih could be discriminative) and has to deal with the problem of needing data to inspect the intermediate, generative model (which is also learned using federates, DP guaranteeing ways). It is not at all clear what one accomplished by doing this. 
* GANs are notoriously hard to train with mode collapse etc.  Setting hyper parameters of any generative model also needs access to original data and impacts the privacy guarantees. 
***NOTE added after author response***
The rebuttal has sufficiently address the quibbles I raised below. I'm leaving it here to allow traceability. I'm not fully convinced about the response to the main issue I raised above (ie if the generative model is not very representative, high-fidelity, then one cant know whether a potential bug discerned by inspecting its samples is an artefact of the generative model or whether it is truly a fundamental bug upstream -- and training a high quality generative model also requires one to inspect the raw data in the first place so the problem has simply been swept under the carpet). Nevertheless for a first paper on the topic I think the contributions and intuition provided here are quite valuable so I am ok leaving this for for future work. 
* quibble#1: theoretical DP bounds are not very tight. For example, in table 2 they may want to use realistic estimates instead of epsilon even to prove their high level point. I'm not sure I can buy their argument even on this illustrative problem as it stands. 
* quibble#2: You may want to at least make an effort to compare against the nearest possible methods in your experimental setup even if they are not a great match to the problem. I'm not intimately familiar with the recent literature but you mention Triascyn & Faltings (2019) so perhaps you could also use that and expand a bit more on the novelty here