PONE-D-24-02004Sparsified Federated Learning with Differential Privacy for Intrusion Detection in VANETs based on Fisher Information MatrixPLOS ONE

Dear Dr. Chen,

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Mohammed Balfaqih

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper introduces FFIDS, a novel approach for enhancing the security of VANETs through the integration of Federated Learning (FL) and Differential Privacy (DP). The primary focus is on addressing the challenge of preserving privacy in FL while minimizing the impact on model accuracy and communication costs. FFIDS employs a unique combination of model parameter pruning based on the Fisher Information Matrix and DP techniques. By identifying and pruning unimportant parameters, it reduces privacy budget consumption per iteration without sacrificing accuracy. DP noise is selectively added to submodels, prioritizing important parameters, thereby optimizing privacy preservation. The proposed approach is validated through extensive experiments on public and simulation datasets, demonstrating its effectiveness and superior performance in VANET intrusion detection models. However, some issues may need more attention.

1. Line 21 of the manuscript should be revised to replace "Inference attacks" with "member inference attacks." Additionally, there is a grammar mistake on line 14 that needs correction.

2. Line 189, should "g(w, D)" be changed to "s(w, D)"?

3. Line 211, should "Z[j]" be changed to "Z[i]"?

4. Line 218, should "wi" be changed to "w[i]"?

5. Is the loss function "P(y|x; w)" or "f(w)"?

6. In the experimental section, there are a few issues may need further considerations:

(1) Firstly, Table 2 should not exceed the length specified for the main text. Secondly, the data in Table 2 appears perplexing. Why are only NbAFL, DPFL-SR, and FFIDS included as methods? Additionally, there is no presentation of comparative results for communication overhead based on matrix update frequencies. Finally, I am particularly interested in how communication overhead is measured.

(2) In Figure 4, why does LDP-FedSGD outperform FFIDS? LDP-FedSGD does not involve parameter selection, and my understanding is that this approach should introduce more negative impacts. I am also puzzled by the fact that LDP-FedSGD in Figure 5 seems unaffected by the privacy budget.

(3) I suggest including a comparison of communication overhead with quantization-based methods, such as cpSGD.

Reviewer #2: This paper presents a privacy-preserving and communication-efficient Federated Learning algorithm for intrusion detection in VANETs. The primary concept revolves around utilizing the Fisher Information Matrix to evaluates the importance of model parameters, further pruning the model to retain important model parameters and adding DP noise only into these important parameters for diminishing the private budget consumption. Preliminary experiments are conducted to demonstrate the efficacy of the proposed approach on two public datasets and two F2MD simulated datasets. The suggested approach is acceptable, albeit with several critical concerns that require attention:

1)due to the involvement of gradient pruning in the proposed method, it is necessary to include relevant content in order to align the gradient pruning method with theoretical foundations in Section 3.

2)Please note that there is a error in Table 2 of the experimental results.

3)It is advisable to provide meaningful citation locations for references, for example, a method [21]. Please check for duplicate citations in the order of references.

4) Finally, please improve the quality of the paper by correcting grammatical errors and spelling mistakes, such as a spelling error in the left image of Figure 1.

Based on the comments above, I suggest making the necessary modifications before publication.

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Reviewer #1: No

Reviewer #2: No

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Sparsified Federated Learning with Differential Privacy for Intrusion Detection in VANETs based on Fisher Information Matrix

PONE-D-24-02004R1

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Mohammed Balfaqih

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all my previous concerns. Specifically, the corresponding symbols in the paper have been updated, and experiments have been supplemented promptly. The reviewer recommends an accept.

Reviewer #2: All comments have been addressed. The proposed approach is technically sound and the experimental results can suport the conclusion in this manuscript. I suggest this paper may be acceptable.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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