Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper talks about use of federated learning to generate handwritten text. It also presents code link along with generated results. 
strengths_and_weaknesses: Strengths:
1. Everything is presented clearly along with the core idea
2. Results are also presented along with a discussion of future directions

Weakness:
1. Experiments have not been performed along with analysis of other methods and comparison studies. 
2. Lack of quantitative studies
suggested_changes: Quantitative studies along with comparative analysis can make this a good result.
Some detailed analysis of generated images with potential ideas to improve the results can present a good study.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: Authors propose training VAE in federated learning(FL) fashion to reconstruct/generate MNIST data. Promising visuals for reconstructing MNIST data are presented.
strengths_and_weaknesses: * **Strengths**
    * An hybrid approach combining VAE and Federated Learning(FL)
    * Qualitative visuals seeming close to standard offline VAE
    * The experiments are reproducible given the code is available.
* **Weaknesses**
    * A objective comparison of quantitative metrics is missing
suggested_changes: Besides qualitative visuals having an objective comparison of quantitative metrics is invaluable to this submission. Still I would recommend this work discussions at ICLR.
review_title: null

Meta-review: metareview: This work proposes a federated learning-aided VAE. This work is not solid and not interesting. Also, experiments are not convincing. The presentation is not clear.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to revise