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 proposes a new compression method using knowledge of rate–distortion theory. Several experiments were conducted to show how the proposed method outperforms several state-of-the-art methods in the accuracy-rate trade-off. 
strengths_and_weaknesses: S: A new compression method for FL that (empirically) outperforms the state-of-the-art methods. Ablation studies also strengthen the paper.
W: Missing formalization but this is explicit in the paper, which can also be investigated in future work.
suggested_changes: - The acronym FL in the abstract is not used within the abstract and can be removed;
- What is d in Algorithm 1? Please define it.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: Rate-distortion theory was utilized to present a compression method. This method is claimed to reduce communication costs in Federated Learning. An algorithm is presented to design the encoder operator for clients.
strengths_and_weaknesses: Strengths :

* The paper addresses communication efficiency which is an important component in FL
* Empirical results were provided for variety of datasets along different modalities


Weaknesses :

* It appears the proposed algorithm addresses average case.
* Emperical results on large scale datasets are not available.
suggested_changes: The authors can also extend and produce results for large scale datasets and settings. This can definitely enhance the impact of this work.
review_title: null

Decision: Invite to present