Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning

Kimleang Kea; Youngsun Han; Tae-Kyung Kim

doi:10.1371/journal.pone.0290337

Peer Review History

Original SubmissionMarch 28, 2023
9 Jun 2023 Decision Letter - Letterio Galletta, Editor PONE-D-23-09375Enhancing Anomaly Detection in Distributed Power Systems using Autoencoder-based Federated LearningPLOS ONE Dear Dr. Kim, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands.Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please take into consideration all the reviewers' comments while preparing the revised version of your manuscript. Moreover, be sure to provide more details of your dataset and to make it publicly available for the sake of reproducibility of the study. Please submit your revised manuscript by Jul 24 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ******** 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: In this manuscript, the authors present a federated learning framework for anomaly detection in distributed power systems. With the exception of some grammatical errors, the paper is relatively well written and technically sound. My general comments may be summarized as follows: * Please ensure that all acronyms that are used more than once are defined at first occurrence; those that are used only once need to be spelled out. * For the sake of clarity, vectors and matrices should be denoted by boldface lower- and upper-case letters, respectively. * English usage, grammar or spelling errors require some copy-editing. * Line 12 should be rephrased as "{0,1} with 0 and 1 representing “normal” and “anomalous” data points, respectively." * Eq. (3) is unnecessary and should be removed. * Line 253: delete "or mean squared error". * All figures should be included in the text (not at the end of the paper). * Eq. (12): How is the loss function defined? what are O_i and \\hat{W}_i? and what is the difference between the inputs X and W? * The labels in the figures are too small and virtually unreadable. * The runtime analysis of the proposed algorithm needs to be discussed. * It is unclear how the hyper-parameters affect the overall performance of the proposed approach. Reviewer #2: Please provide more details of the dataset. Is it publicly available? "distributed power system" in the title is misleading as the paper actually uses a home energy consumption dataset. Please provide more justifications for the use of FL for this application. ******** 6. 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? 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0290337.r001
Revision 1
19 Jun 2023 Author Response To: PLOS ONE Editor Re: Response to reviewers We are writing to express our gratitude for the time and effort you have dedicated to reviewing our paper, "Enhancing Anomaly Detection in Distributed Power Systems using Autoencoder-based Federated Learning." We deeply appreciate the constructive feedback and suggestions you have provided, which have significantly contributed to the enhancement of our work. We have thoroughly considered all your comments and suggestions and have made the requisite amendments to our manuscript accordingly. We believe that the revised paper effectively addresses all the issues brought up in your review, and we are hopeful that it aligns with your expectations. Once again, we wish to express our gratitude for your time and consideration. We eagerly anticipate any further feedback you may provide. Thank you for your continued engagement with our work. Sincerely, Kimleang Kea, Youngsun Han, and Tae-Kyung Kim Reviewer#1 Comments: In this manuscript, the authors present a federated learning framework for anomaly detection in distributed power systems. With the exception of some grammatical errors, the paper is relatively well written and technically sound. My general comments may be summarized as follows: • Please ensure that all acronyms that are used more than once are defined at first occurrence; those that are used only once need to be spelled out. • For the sake of clarity, vectors and matrices should be denoted by boldface lower- and upper-case letters, respectively. • English usage, grammar or spelling errors require some copy-editing. • Line 12 should be rephrased as "{0,1} with 0 and 1 representing “normal” and “anomalous” data points, respectively." • Eq. (3) is unnecessary and should be removed. • Line 253: delete "or mean squared error". • All figures should be included in the text (not at the end of the paper). • Eq. (12): How is the loss function defined? what are O_i and \\hat{W}_i? and what is the difference between the inputs X and W? • The labels in the figures are too small and virtually unreadable. • The runtime analysis of the proposed algorithm needs to be discussed. • It is unclear how the hyper-parameters affect the overall performance of the proposed approach. Author response: We sincerely appreciate your thorough review of our manuscript, and we are grateful for your valuable comments and suggestions. Author action: We have thoroughly deliberated on each of your observations and implemented the requisite modifications in response to them. The following are the amendments we have carried out in light of your feedback: • We have taken care to define all acronyms at their first occurrence when they appear more than once in the text. For acronyms used only once, we have elected to spell them out fully to ensure clarity. • To enhance readability and clarity, we have now denoted vectors and matrices by using boldface lower-case and upper-case letters, respectively. • We have meticulously reviewed the manuscript for any errors related to English usage, grammar, and spelling to ensure the clarity and accuracy of our content. • Upon re-evaluation of Line 12, we were unable to locate the specific reference mentioned in your comment. • We have determined that Equation (3) was not essential to our discussion and have subsequently decided to remove it from the manuscript. • We have excised the phrase "or mean squared error" from line 253 to mitigate any potential confusion. • In an effort to enhance the narrative flow of the paper, we have now incorporated all figures directly within the text. • Upon reexamination, we acknowledge that Equation (12) may have been misinterpreted due to its presentation in the initial version of our manuscript. To rectify this, we have revised Equation (12) to ensure its coherence with the rest of the equations in our manuscript. • We have rectified the issue regarding the small and unreadable labels in the figures; we have ensured that all labels are now clearly visible for ease of understanding. • The computational efficiency of the proposed algorithm is now demonstrated through a runtime analysis, as presented in the Ablation Study section, and is further expounded upon in our discussion. • We have now included a comprehensive discussion on the impact of hyperparameters on the overall performance of our proposed approach, aiming to provide a thorough understanding of the model's behavior. Reviewer#2 Comments: Please provide more details of the dataset. Is it publicly available? "distributed power system" in the title is misleading as the paper actually uses a home energy consumption dataset. Please provide more justifications for the use of FL for this application. Reviewer#2, Concern # 1: Please provide more details of the dataset. Is it publicly available? Author response: We appreciate your inquiry concerning the dataset. In response to your query, we would like to confirm that the dataset utilized in this study is indeed publicly accessible. The dataset can be accessible from the following address: https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption Author action: In response to your query, we have updated the manuscript to include appropriate citations for the datasets used in our study. Our intention is to provide a more transparent account to the readers regarding the sources of our datasets. Reviewer#2, Concern # 2: "distributed power system" in the title is misleading as the paper actually uses a home energy consumption dataset. Please provide more justifications for the use of FL for this application. Author response: We truly value your constructive comments and insights regarding our study. Indeed, we employ a home energy consumption dataset for our analyses. However, within the context of the Federated Learning (FL) framework, we use this dataset to emulate a "distributed power system" scenario. During this simulation, we partition the dataset into separate segments and allocate these to individual FL clients, each of which then operates as an autonomous power system. Each FL client serves to locally train models and subsequently consolidate their knowledge to the FL server in distributed manner. Author action: We have taken your feedback into thorough consideration and have made substantial amendments to provide clarity on the usage of datasets in the context of the distributed power system, specifically in Section 5.1.1 of our manuscript. In the updated version, we have elucidated the relevant details to dispel any potential misunderstanding related to this matter. We are confident that these revisions successfully address your concerns and offer a lucid understanding of the application of datasets in our study. Attachments Attachment Submitted filename: PLOS-ONE-Response-to-Reviewers.docx https://doi.org/10.1371/journal.pone.0290337.r002
4 Aug 2023 Decision Letter - Letterio Galletta, Editor Enhancing Anomaly Detection in Distributed Power Systems using Autoencoder-based Federated Learning PONE-D-23-09375R1 Dear Dr. Kim, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Letterio Galletta 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 ******** 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 ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A ****** 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 ****** 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 ****** 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: Most of the points raised in my first review have been satisfactorily addressed in the revised manuscript. ****** 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 ******** https://doi.org/10.1371/journal.pone.0290337.r003
Formally Accepted
9 Aug 2023 Acceptance Letter - Letterio Galletta, Editor PONE-D-23-09375R1 Enhancing Anomaly Detection in Distributed Power Systems using Autoencoder-based Federated Learning Dear Dr. Kim: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Letterio Galletta Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0290337.r004

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