Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper proposes a use-case for hypernetworks in personalized federated learning for segmentation of prostate MRI scans
strengths_and_weaknesses: I would first like to commend the author(s) for this work especially given that this is a first-time submission to an academic conference. This is a well-structured, concise paper that is supported nicely with suitable experiments. 

Clarity :
- findings are communicated clearly and effectively.
- relevant literature has been included though this section can be improved.

Reproducibility: Code not provided. Publicly available datasets have been used.

Follows basic requirements: Yes

Correctness: The paper provides the technical details of the implementation. It is a good start and can be improved further by addressing these comments:

- the claim that "We describe the first HN-based PFL for segmentation of medical images" may not hold true (though I am happy to be proven wrong) - please refer to the references given in the next section. Especially for the first reference (Wang and Jin et al.), please highlight the differences between their approach and the framework proposed in this paper.
- In the HyperSeg Conv setup, was also the 'HyperSeg Conv + Embeddings' setup evaluated ? If yes, please include them in the results table.
- If possible, in the evaluation metrics, please include IoU (for regions) and ASSD scores (for boundaries) - again, refer to the 1st reference in the next section.
- For the training, please mention the hardware details and the training/inference times.
- Does the learning rate stay constant or does it reduce as training progresses ?
- Have the authors explored more recent approaches such as Split-learning that could be used with HN instead of Federated SGD ?

suggested_changes: 1. Please refer to the following references and add them to the paper where necessary:
- regd segmentation (minus HN). This submission (to tiny papers) focuses on using PFL+HN settings that have been used for classification and extending them to a segmentation task. This paper would be extremely helpful to refer to the opposite - using the settings that work for prostate segmentation + pfl and combining them with HN: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136810449.pdf
- split learning: https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf
- FL with HN but classification (models used): https://www.nature.com/articles/s41598-023-28974-6
- FL with HN for reconstruction (requires larger models): https://arxiv.org/pdf/2206.03709.pdf

2. We thank the authors for the kind words in the last paragraph in their URM statement. As a suggestion, I would recommend having this paragraph as a footnote if there are any space constraints in case any of the review comments are incorporated.

3. Please mention the hardware details and training times. Also, please address some of the points mentioned in previous section under the 'correctness' sub-section.

If the comments are addressed or clarified reasonably, I would be happy to consider upgrading the assigned score.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper presents a preliminary case study that applies Hypernetworks and Personalized Federated Learning towards solving segmentation task in medical images. The main contribution of the work is to verify empirically that such setting, which has been tested successfully with easier classification tasks, can be extended to more complicated segmentation task.
strengths_and_weaknesses: Strengths:
1. The paper is well-written, explains the problems and the settings well.
2. Though straightforward in terms of ideas and research directions, the presented study may be more valuable to the applied medical tools.

Weaknesses:
1. The idea is very simple, given that the HN+PFL setting has been tested as helpful for the classification problems, testing the exact same setting just with a segmentation task seems a bit lack of novelty.
suggested_changes: The paper provides a great start for doing research in applying federated learning within medical image analysis, I think that the work presented in this paper serves more like a baseline model (existing setting + new task) for authors to developing a new, better setting for this new task. There isn't much to change slightly for the current version, but I encourage the authors to keep pushing for new results.
review_title: null

Meta-review: metareview: The paper explores the use of hypernetworks and personalized federated learning for medical imaging. It introduces the premise and experimental setup clearly. The level of detail provided is admirable making this work reproducible.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present