Thank you for your further comments.
For the details of the review:
1. FedFA aims to augment the feature statistic by constructing a vicinity Gaussian distribution for each data point and change the empirical risk to the vicinal risk. Such an augmentation operation can enrich the feature information after the feature extractor layer. However, this approach has its limitations, especially in cases where data volume is small or distribution is uneven. In these scenarios, the constructed Gaussian distribution might not fully capture the true diversity of the data and can have bias. Also, it requires additional computation regarding the distribution parameters locally for each feature extractor layer with augmentation. In contrast, our proposed FedPLVM utilizes only the feature representation of the final feature extractor layer to cluster several local prototypes regardless of the even or uneven feature distribution and further performs global clustering and combines with the $\alpha$-sparsity loss in the local training. Lastly, FedFa is one of the baselines we compared in our paper and the result shows our method outperforms FedFA in Digit-5, Office-10 and DomainNet.
2. We appreciate your suggestions regarding the organization of our paper. However, we must respectfully disagree with the comment that excessive space is devoted to preliminary discussions at the expense of comparisons with existing methods. Firstly, the preliminary section occupies only half a page and concisely presents essential knowledge required for understanding our proposed method. This information is crucial and cannot be further condensed without loss of clarity. Secondly, we have integrated comparisons with existing methods throughout the introduction and methodology sections. Specifically, our comparison with FPL, which is most relevant to our work, includes dedicated paragraphs discussing the differences (see lines 232-247).
For Questions:
1. We appreciate for suggesting the paper on FedOTP. While FedOTP is indeed a noteworthy contribution, we believe it addresses a different aspect from that of our paper. FedOTP focuses on developing efficient collaborative prompt learning strategies to integrate pre-trained visual-language models into federated learning frameworks. This topic, although valuable, is not closely related to the core focus of our research.
2. The icon in Figure 1 of our paper mentioned in your question is not that of a monitor but a laptop. In FL, the client device doesn’t necessarily need to capture the data itself. In our illustration, the laptop processes the stored digital data without needing to capture figures or photos directly.
3. We thank you for your advice on including a convergence speed analysis. We agree that theoretical convergence analysis is a crucial aspect of federated learning. Typically, such analyses in existing FL research depend heavily on strong assumptions about gradient bounds and the differences between global and local loss functions. Importantly, incorporating prototype learning alters the loss function itself. Since FL convergence analyses require the specific assumptions regarding the loss functions, the advantages of integrating prototype learning into the loss function might not be readily apparent. This limitation is also why existing FL studies that utilize prototype learning, such as FPL, do not provide theoretical convergence analysis. Instead, to address concerns regarding convergence speed, we have provided experimental results that clearly demonstrate the improvements our method achieves. In our Digit-5 experiment, we performed 100 global rounds as stated in our paper and compared the convergence speed with and without our proposed $\alpha$-sparsity loss. We reported the average test accuracy after each 10 rounds training. Obviously with the $\alpha$-sparsity loss we have a faster convergence speed (70 rounds compared to 80 rounds without the proposed loss function) and a higher converged accuracy:
| Round | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| w/o $\alpha$-sparsity loss | 35.14 | 56.44 | 57.58 | 62.20 | 64.90 | 65.72 | 65.02 | 66.66 | 66.32 | 66.06 |
| w/ $\alpha$-sparsity loss | 54.12 | 62.44 | 65.32 | 66.12 | 67.32 | 68.20 | 69.62 | 69.22 | 69.86 | 69.26 |
4. We appreciate your suggestion to include visualizations to support our claims. However, during the author-reviewer discussion period, we are unable to add new figures. Should our paper be accepted, we will certainly include the recommended visualizations in the final version of the manuscript as advised.