Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: - This work proposes a classifier calibration technique to handle the data heterogeneity commonly encountered in federated learning setups.
- This work motivates its proposal by assessing the similarities of representations learned in different layers of the network, and finds that the final layer (classifier layer) is the one most dissimilar across clients.
- They propose a post-learning classifier calibration technique through representations sampled from a gaussian mixture model estimated from representations from client data.
- Experiments on CIFAR-10, CIFAR-100, and CINIC-10 with FedAvg, FedProx, and MOON show improvements over models learned with no calibration.
main_review: **Strengths**

- The paper is very well written with a clear flow of ideas and adequate references.
- The experiments are well motivated with the prior experiment clearly leading to the next.
- The insight that the earlier layers in a network tend to learn similar features while the last layer has the most dissimilarity is very interesting and could motivate future work in this area.


**Improvements**

- Table 1: While `clsnorm` and `clsprox` are understandable on closer study, it might help to map these terms to relevant sections in Sec 3.3
- Figure 6: It will be helpful to overlay all three images on one another to enable the reader to understand the difference in heterogeneity on a common scale. Currently, with difference Y-scales for the three images, it is not easy to compare them with one another
- Figures 4 and 6: It will also be helpful to have error bars on these figures.
- There are minor typos in some places which the authors should go through and fix in the updated manuscript
limitations_and_societal_impact: - The authors discuss the limitations of their method in Section 5.3
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: This paper proposes to bridge the gap between data heterogeneity and layer-wise sensitivity in deep models in Fed learning. The contributions of this paper are as follows: (1) the authors claim to be the first systematic study on hidden representation sensitivity to IID noise, the result of which reveals that the classifier layer is the most sensitive. (2) the authors then propose a calibration procedure that preserves the privacy of inputs while dealing with mitigating data drift. 
main_review: Strengths: 
- Reasonable summary of related work, broad synthesis of categories related to heterogeneity mitigation is useful.
- The proposed method is very simple and easy to understand. 
- Great reproducibility; supplemental material provides code and data for all experiments. 

Weaknesses:
- One of the main selling points is that the authors fit a GMM on the last representation layer before the classifier from which to draw samples for calibration. The authors suggest that the alternative approach of directly using hidden representations from actual samples violates privacy. However, fitting a GMM to the last layer essentially leaks information about the distribution (i.e., the sufficient statistics) from which the client data is generated. In this sense, it is unclear whether the proposed approach actually respects the privacy constraint. 
- Weak experimental baselines. Only FedAvg, FedProx, and MOON are used for comparison. No baselines from personalized federated learning or aggregation schemes are included. In particular, a more fair comparison should consider personalized federated approaches such as meta-learning, which is in principle similar to the proposed GMM + calibration approach. 
- Limited technical contribution. Why use GMM in favor of other (more powerful) generative models? Section 5.3 mentions the separability of learned representations. If a GMM is used to generate synthetic samples, perhaps a more rigorous analysis can be provided to formalize 5.3 findings (i.e., performance guarantees or limitations based on GMM parameters, etc).

Overall the novelty is not very high and experiments do not make a strong case for a NIPS paper.
limitations_and_societal_impact: N/A
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors proposed CCVR to post-calibrate federated learning classifiers models. Insights were first drawn from preliminary experiments, and CCVR is then tested atop many popular FL methods to demonstrate its efficacy.
main_review: The idea of using simple statistics to calibrate federated learning models is great. Especially when the authors were able to show prominent gains with a post-hoc method that does not require modifications to existing training methods. The field of FL generally lacks this type of insightful yet powerful research.

With that said, I am providing some comments below:

- This work is missing a critical reference: [1] addresses client distribution drift on large scale FL datasets. It falls under the first category in L28.

- It seems a bit indirect having to aggregate per-client Gaussian mixtures into a global Gaussian mixture. One might argue that this step removes per-client statistics in exchange for better privacy, but in practice the clients might still need to send the full matrices over to the server for the aggregation. Hence it is reasonable that a server utilizes these statistics for learning/fine-tuning. I wish to either see the authors address this privacy comment, or provide some insights on how per-client CCVR (sampling from per-client Gaussians) compares with global CCVR (sampling from global Gaussian)

- In the case of extremely non-identical client distribution such as when alpha -> 0, some clients might hold very sparse classes, and the mean/covariance would be mostly represented by a few samples. The statistics may expose a lot about the client’s single examples. 

- Although this work investigates a general phenomenon about FL, the datasets used (CIFAR/CINIC) are still very basic datasets that were not intended for realistic non-identical setups. Sure the datasets can be synthesized to have non-identical clients, by the conventional Dirichlet assignment neglects the fact that client data pool sizes can be drastically different. See [1]. An additional verification on larger datasets would be very helpful.

- Isn’t section 3.3 simply “data sharing for FL model improvement”? If it is, I am receiving contradicting messages from the Introduction and the analysis in this section.

- It’ll be good to provide a brief analysis on the sensitivity against µ, the regularization coefficient.

- In table 1 clsnorm / clsprox is not clearly described. Consider including the names in section 3.3

- Figure 5 does not convey the message effectively. The embeddings are basically identical across rows and the differences are exhibited in the colors. Consider using confusion matrices.

[1] Federated visual classification with real-world data distribution.

I am happy to increase my rating should the comments be addressed properly.
limitations_and_societal_impact: I recommend discussing more limitations should the space allow.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper claims that current federated learning (FL) systems suffer from performance reduction due to high variance in the features learned by the final layers of the network across clients. The authors motivate their approach by training a local model per client and observing the representations learned by each layer. They show ​that the first layers generate similar representations across clients while the representations of more advanced layers are less similar. Therefore, they propose a simple post-processing step to correct the weights of the classification layer of the shared model after training with standard FL procedures (such as FedAvg). They assume the representation in the last layer follows a Gaussian distribution per class.  At the end of the training stage, they store statistics of the representations in the last layer for each client. Based on these statistics, they generate pseudo-data on the server and retrain only the last layer. The final model composed of the initial layers (that were trained regularly with standard FL approaches) and the final layer that was relearned.
main_review: Overall it is a nice paper; however, I don't think that it is original or significant enough to be accepted to NeurIPS. I am willing to change my mind in light of new information from the authors and by addressing the below comments.  

This paper presents the following merits:  
- The approach is simple and doesn't impose a large overhead over current learning procedures.  
- The approach is general in the sense that it can be integrated with many FL learning procedures.  
- In CINIC-10 the improvements are significant over compared baseline methods.  
- The experimental setup is clear. Code was provided which is great.  
- The analysis of the method is good and exhaustive.  

Some points that I found problematic:  
- I think that the comparison made in the paper to baseline methods is severely lacking. Therefore, it is hard to accurately quantify how significant the improvements over current methods really are. Many studies were done in the past few years showing that under heterogeneous distribution FedAvg does not perform well (e.g., [1]). A comparison to more recent baselines (even ones that can not be integrated with this method) is needed.  
- All the results in the paper are with 10 clients. Real-life federate learning systems have much more than that. I believe that the performance should be tested under a varying amount of clients.  
- The results on CIFAR-100 indicate that this method will no perform well when the data per client is limited. This point was also stated by the authors and I appreciate that. However, federated learning is of most use under this scenario exactly, when there is enough data per client one may do local training only. I think that this point weakens the paper's claim.  
- Although the analysis made in the paper showing that the representations of later layers are less similar across clients is nice, I think this is not surprising, especially given that there isn't a domain shift across clients. This is in line with the current understanding of NNs that early layers learn general (and transferable) representations while the representation of later layers is more specific [2].  

Some minor comments:
- The authors cannot expect the average user to be familiar with Centered Kernel Alignment (CKA) and should provide a detailed description about it in the appendix.  
- I think that the colors in the t-SNE figures should be associated with the actual classes (and refer in the text to points associated with that class by the class name, e.g., L297).
- Some references should be altered from arXiv to the publication venue (e.g., [3], [4]).


[1] Arivazhagan, M. G., Aggarwal, V., Singh, A. K., & Choudhary, S. (2019). Federated learning with personalization layers. arXiv preprint arXiv:1912.00818.  
[2] Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014, December). How transferable are features in deep neural networks?. In Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2 (pp. 3320-3328).  
[3] Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.  
[4] Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., & Khazaeni, Y. (2020, April). Federated Learning with Matched Averaging. In International Conference on Learning Representations.  
limitations_and_societal_impact: The authors addressed the potential limitations and negative societal impact of their work.
review_title: null

Meta-review: The reviewers appreciate the simple yet effective strategy in this paper that handles heterogeneous data in federated learning. The experimental finding that the last layer exhibits the most dissimilarity could inspire future research. Great reproducibility and good writing also make the paper stand out. Therefore, I recommend acceptance. In addition, please incorporate the new results in the final version.
Decision: Accept (Poster)