reviews: 
Comments: This paper proposed a novel personalized federated learning method called FedSimSup, which addresses the issue of data heterogeneity by introducing local supervisors to assist in model training and combining personalized models for global information aggregation. The method considers the similarity relationships among clients and enhances information utilization efficiency through weighted personalized models. Experimental results show that FedSimSup outperforms seven state-of-the-art federated learning methods when handling heterogeneous data.
Strengths: 1) The FedSimSup method introduces the concept of local supervisors into traditional personalized federated learning, providing a new approach to address the issue of data heterogeneity. 
2) The baseline methods are diverse: through comparisons with various federated learning approaches, FedSimSup demonstrates strong performance. 
3) FedSimSup demonstrates a certain level of interpretability in its information utilization.
Weaknesses: 1)The theoretical support of the paper is insufficient. Although the FedSimSup method is proposed, there is a lack of in-depth analysis of its theoretical foundations. For example, key issues such as how to ensure the effectiveness and stability of the similarity measurement, as well as the specific impact of the supervisor model on the final model's performance, are not adequately addressed.
2)In Section 3.2, to address the issue that "clients cannot determine whether the received model (containing global information) is more beneficial than the model trained in the previous round," the authors divide the model into a supervisor and a personalized part. However, it is unclear why, after this division, clients can assess whether the received information is more advantageous. What metrics are used to quantify this judgment? The authors should provide necessary explanations.
3)In Section 4.2, the performance of FedSimSup under the Pathological distribution is poor. The authors attribute this to the limited discrete values resulting from similarity calculations under the pathological distribution, which affects the differentiation of similarity between clients. This explanation lacks theoretical and experimental support. It is recommended that the authors conduct a more in-depth discussion and analysis.
4)The specific implementation of similarity measurement is unclear. The paper mentions using cosine similarity to calculate the similarity between clients, it lacks detailed explanations on how to specifically implement and optimize this process. There is also no discussion on how to maintain the stability and accuracy of similarity calculations under different label distributions.
5)The connections between various components are still unclear, and there is a lack of ablation studies. The paper does not conduct ablation experiments to verify the impact of different components (such as the separation of the supervisor and personalized model) on overall performance, making it impossible to determine the importance and necessity of each part.
6)The complexity of the model has not been adequately considered. The paper mentions that different clients can use different supervisor architectures, it does not provide specific guidance or standards for selecting these architectures. Additionally, it does not discuss the impact of model complexity on training time and resource consumption.
7)The evaluation of privacy performance has not been considered. Although the paper discusses personalized learning and model aggregation, there is limited consideration of privacy protection. It lacks a discussion on how to effectively utilize similarity information while ensuring client privacy.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2