Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper explores a new approach called FedKernel to address different heterogeneity in FL. This approach tries to reparameterize the convolution kernel into multiple sparse kernels. This can further enhance the robustness of federated learning. 
strengths_and_weaknesses: **Strengths:**

S1: The approach of using sparse kernels for federated learning is novel. And this approach is general enough to be easily adapted into different SOTA FL algorithms (FedAvg, FedProx and Moon etc).

S2. This paper is well written. The logic is easy to follow. All the findings in the paper is expressed clearly and effectively.

S3. Experiment setup (dataset, Non-IID partition, baseline algorithms) is clear and easy to be reproduced. Experimental results are strong enough to cover the statements in the paper.

S4: The claims and conclusions are justified by the findings. Authors follow the formatting requirements. 

**Weakness:**

W1: Authors can explore different kernel size and different datasets to make the experiment stronger.  Also, authors should state the number of devices in their experiment setup.

W2: Compare to the normal 7x7 kernel,  why this kind of kernel with combined sparsity (7x3, 3x7, 3x3) can result in better accuracy? Authors could give more explanation about this.

W3:  Authors should release their source code if possible.


suggested_changes: As stated in weakness, some improvements could be:

C1: as W1, add more details about the experiments.

C2: Please consider to cite the following related work in introduction.

[1] FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

[2] Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks

[3] Federated learning on non-IID data: A survey

[4] Real-world image datasets for federated learning
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This work introduces FedKernel, a sparse convolutional kernel approach for models in the Federated Learning setting, which seems to improve generalisation to distribution shifts and transferability. The work outperforms existing baselines that do not use FedKernel on mini-ImageNet with a ResNet-34
strengths_and_weaknesses: Hello authors, thank you for choosing to participate in Tiny Papers this year. Here are some comments:

**STRENGTHS**
- Clear description of the method and what it brings to the table, especially in the FL vision space
- Evident from the results the impact of FedKernel on generalising across different non-iid settings
- Novel idea that has the potential to become a full paper if more experiments and ablations are included
suggested_changes: No breaking changes, meets the standard for Tiny Papers.

> **URGENT:** If you haven't already, please include the URM statement in the camera-ready version.
review_title: null

Decision: Invite to present (notable)