reviews: 
Comments: This paper presents a personalized federated learning method named FedSimSup, designed to address the challenges posed by heterogeneous (Non-IID) data across clients in federated learning. Traditional federated learning methods may struggle with model weight divergence when clients have diverse data distributions, degrading performance. FedSimSup aims to tackle this challenge by introducing a local supervisor for each client to ensure the personalized model remains aligned with local data. This supervisor can override global updates if they are not beneficial, effectively balancing the integration of global and local knowledge.

Key contributions include the following ones. 
1. FedSimSup assigns a unique local supervisor to each client, allowing the model to selectively integrate global updates based on their relevance to the client's data. If an update isn't beneficial, the supervisor maintains the client model's previous state, enhancing performance with minimal communication rounds. 
2. FedSimSup uses label distribution similarity between clients to enable selective information sharing, allowing clients to learn primarily from those with similar data distributions, improving model personalization. 
3. It supports various model architectures across clients, adapting to the specific computational capabilities and needs of each client. The model also provides interpretability through Class Activation Maps that visualize the supervisor's influence in keeping model attention aligned with relevant features.
Strengths: See the contributions in the summary. 

Also, two highlights of strengths:
1. Information among similar clients are utilized.
2. local model aggregation is somehow effective.
Weaknesses: 1. Insufficient Justification for Additional Model: It's needed to clarify the necessity of adding a local supervisor model that frequently participates in training. Note that there are many approaches to avoid adding a trainable model for each client, which would significantly increase resource consumption in terms of space and time.

2. Unclear Model Heterogeneity: This work mentions that the proposed method enables model heterogeneity to accommodate different mobile devices. However, it appears that heterogeneity is only considered for the supervisor model which may not be necessary. Typically, model heterogeneity applies to the client's primary model (i.e., the personalized model) or to the entire client framework. Since this work focuses on the supervisor model alone, the contribution regarding model heterogeneity is unclear and not well-justified.

3. Unclear contributions regarding explainability: This work uses CAM to demonstrate only explainability rather than interpretability. Adding external XAI mechanisms to enhance explainability is not an inherent advantage of the proposed method itself.

4. Unclear Data Heterogeneity: The paper uses Dirichlet and pathological distributions but should clarify which specific type of heterogeneity the method targets, as data heterogeneity includes many categories. Besides, even within Dirichlet and pathological distributions, different parameter settings have distinct real-world implications, which should be specified in the introduction.

5. Methodology Clarity: The motivation behind the supervisor's role is unclear, as is the explanation of similarity information. Figure 1(a) could be confusing, particularly regarding the communication rounds.

6. Lack of Theoretical Analysis: Although convergence is demonstrated experimentally, this paper in the present form lacks theoretical analysis of the method.

7. Some issues in experiments and results:
7.1 Unconvincing Baseline Selection: The baseline choices are either insufficiently targeted or poorly explained. Baselines with similar designs or research objectives should be selected for comparison.
7.2 Lack of SOTA Performance: As shown in Figures 1 and 2, the proposed method is not always optimal on pathological distributions, and its advantage on Dirichlet distributions is not always prominent.
7.3 The results shown in Tables 1 and 2 need detailed explanation. Please also explain why Per-FedAvg performs so poorly.
7.4 Given the addition of a supervisor, the experiments should include an analysis of time and space complexity.
7.5 Lack of Privacy Protection Discussion: Since model is communicated, it would be necessary to provide some discussion of privacy preserving.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 2