Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The paper suggests a method to improve the personalization performance of federated learning models, with the aim of optimizing the global model separately for local inference by each of the clients..
strengths_and_weaknesses: - While the work was relatively elucidated, the very few references suggest that a more thorough literature review can be pretty beneficial for the work.
- Personalization is the flagbearer of federated learning. FL started with the motivation to train and personalize models even further for users without having to share data. I believe the "problem" stated in this work is a standard approach in FL and not a "problem" for federated learning. FL's most significant issues are still along the lines of communication costs, computational costs, and fairness.
suggested_changes: As referenced in the Strengths/Weaknesses section, I believe that a more thorough review of current techniques is needed to ensure that there is a vacuum for this work direction to fill.
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 proposes a personalized federated learning algorithm which aims to develop the client specific model contrary to only learning a single model applicable to each client.
strengths_and_weaknesses: The idea of personalization is important. A single global model might not be an ideal model for participating clients. 

The idea presented is not well described. The parameters used in the experiments are missing. For example, how many clients, how the threshold has been calculated, whether is there a single threshold or multiple thresholds, what extra computational cost this thresholding will bring, etc
suggested_changes: The paper needs a detailed experimental setup to justify whether the experiments performed in the paper are fair or not. 

The experiments need to be performed with various parameters such as $\alpha$

The paper lacks a literature review and its limitations. This is not the first work tackling the issue of personalization; therefore, it is important to highlight the weakness of the current literature. Further, the comparison with existing algorithms is also needed working in the personalization space.
review_title: null

Meta-review: metareview: Concerns regarding _reproducibility_ have been raised by reviewer `5Vou` (specifically, methodological details). Both reviewers `5Vou` and `uWdB` raise concerns regarding the specific contributions of the paper, which I would categorize under _clarity_. Consequently, I do not deem this paper to be CCR.

confidence: 3: The area chair is somewhat confident
Decision: Invite to revise