FedEmoNet: Privacy-preserving federated learning with TCN-Transformer fusion for cross-corpus speech emotion recognition

Mohammed Tawfik; Razan Ali Obeidat; Saddam Kamel; Njood Anwer Aljarrah; Haneen Hussein Shehadeh; Ahmad Dalalah

doi:10.1371/journal.pone.0342953

Peer Review History

Original SubmissionJanuary 29, 2026
25 Mar 2026 Decision Letter - Viacheslav Kovtun, Editor -->PONE-D-26-04975-->-->FedEmoNet: Privacy-Preserving Federated Learning with TCN-Transformer Fusion for Cross-Corpus Speech Emotion Recognition-->-->PLOS One Dear Dr. TAWFIK, 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 submit your revised manuscript by May 09 2026 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. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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The 68.15% accuracy on CREMA-D requires deeper analysis: b. Provide per-emotion breakdown for CREMA-D (currently only Figure 8 shows confusion matrix with unclear values) c. Analyze why neutral performs poorly (58%) - is this due to cultural differences, dataset bias, or annotation inconsistencies? d. Discuss whether the 30% performance gap fundamentally limits the "cross-corpus" claim e. Consider adding t-SNE visualization of feature distributions across datasets to demonstrate domain shift 2. Data Leakage Verification Please explicitly verify and state: a. Confirmation that no speakers appear in both training and test sets across all datasets b. Details on the "mixed client" composition - what is the ratio of EmoDB to RAVDESS samples? c. Whether any preprocessing (e.g., voice activity detection, normalization) was applied globally or per-client 3. Near-Perfect Performance Scrutiny The 99.07% accuracy on EmoDB warrants additional validation: a. Report confusion matrix values numerically (Figure 7a is difficult to read) b. Perform ablation with reduced training data to test if model is memorizing c. Compare against human-level performance on these datasets d. Discuss whether ceiling effects limit the meaningfulness of comparisons 4. PSO Optimization Details a. How was the fitness function accuracy computed during PSO iterations? Cross-validation on client data? b. The 50 PSO iterations with 20 particles represents 1000 model trainings - this computational cost should be discussed c. Report convergence behavior of PSO optimization 5. Reduce redundancy between Abstract and Introduction 6. Consolidate methodology sections 3.4.1-3.4.4 which are overly fragmented 7. Ensure all figures are referenced in text in order 8. Provide deeper analysis of cross-corpus performance limitations Reviewer #2: Dear Authors, Please consider the following revisions: Major Revisions Required: Elaborate on the multi-scale phase space reconstruction methodology and parameter selection rationale Provide more details on the federated client setup and non-IID data generation process Include additional diverse datasets to strengthen cross-corpus generalization claims Discuss practical deployment challenges and potential solutions Minor Revisions: Clarify the computational efficiency trade-offs and optimization opportunities Expand the privacy analysis to consider additional attack scenarios Provide more comprehensive statistical validation Ensure consistent notation throughout the manuscript Reviewer #3: 1- Clarify the conclusions better, discuss the results, and support them with critical analysis. 2- Clarifying the research gap in the abstract and strengthening it with the datasets used as well. 3- A table should be provided summarizing the relevant works in terms of their strengths and weaknesses, highlighting the research contributions and areas where they surpassed the content of these works. 4- Explaining the difference between the XAI methods presented ****** -->6. 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Revision 1
30 Mar 2026 Author Response Dear Dr. Viacheslav Kovtun (Academic Editor) and Reviewers, We sincerely thank the Academic Editor and all three reviewers for their thorough and constructive evaluation. We have carefully addressed every point, substantially revising the manuscript. The abstract has been rewritten for clarity and stronger gap identification. All figures have been regenerated at 300 DPI and are uploaded separately per PLOS guidelines. Below we provide point-by-point responses. Journal Requirements Requirement 1 — PLOS ONE style and LaTeX template Please ensure that your manuscript meets PLOS ONE’s style requirements. Please update your submission to use the PLOS LaTeX template. The manuscript has been completely reformatted using the official PLOS ONE LaTeX template (Version 3.7, August 2025) with plos2025.bst. Title follows sentence case. Author names, affiliations, and corresponding author email follow the prescribed format. Figures contain captions only (no embedded graphics) per PLOS guidelines. Tables use cell-based formatting without nested tabular environments. References follow Vancouver style. Requirement 2 — Code sharing Please review our guidelines and ensure that your code is shared in a way that follows best practice. All author-generated code underpinning the findings—including the federated training framework, TCN-Transformer model, PSO feature selection pipeline, and explainability analysis scripts— will be made available upon reasonable request to the corresponding author. This is stated in the manuscript. See Data Availability Statement in the revised manuscript. Requirement 4 — Data repository Please upload the minimal data set necessary to replicate your study’s findings to a stable, public repository. 1 PONE-D-26-04975 — Response to Reviewers All three datasets used in this study are publicly available from their original repositories. The Data Availability Statement now provides direct URLs: EmoDB (http://emodb.bilderbar. info/), RAVDESS (https://zenodo.org/record/1188976), and CREMA-D (https://github. com/CheyneyComputerScience/CREMA-D). No custom dataset was created; all experiments are fully reproducible using these public sources. Requirement 5 — ORCID iD Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. The corresponding author’s ORCID iD (0000-0002-1227-387X) has been verified and validated in Editorial Manager. Requirement 6 — Figures 9 and 12 clarity Please upload a new copy of Figures 9 and 12 as the details are not clear. All figures have been regenerated at 300 DPI with larger fonts, clearer labels, and improved color contrast. In particular, confusion matrices now display all numerical values prominently (new Fig 6). The CREMA-D per-emotion breakdown is presented as a dedicated new figure (Fig 7). All figures are uploaded separately as required. Requirement 7 — Reference Tables 1 and 3 in text Please ensure that you refer to Tables 1 and 3 in your text. All tables are now explicitly referenced in the text at first mention. Table 1 (Related Work Summary) is cited in the opening sentence of the Related Work section. Table 3 (PSO feature selection) is cited in the PSO-Optimized Feature Selection subsection. Reviewer #1 Comment 1 — Cross-corpus generalization analysis (a–b) The 68.15% accuracy on CREMA-D requires deeper analysis. Provide per-emotion breakdown for CREMA-D. We have added: New Table 9 with per-emotion Precision, Recall, F1-Score, and Support for all six CREMA-D emotions, annotated by arousal level New Fig 7 with (a) grouped bar chart of per-emotion metrics and (b) arousal-based transfer analysis showing high-arousal at 71.9% vs. low-arousal at 62.1% Clear numerical confusion matrix for CREMA-D in Fig 6(c) See Section “Cross-corpus generalization on CREMA-D,” Table 9, Fig 6(c), and Fig 7. (c) Analyze why neutral performs poorly (58%). Three factors are identified: (1) Cultural and linguistic variation in neutral expression between German (EmoDB), North American English (RAVDESS), and crowd-sourced English (CREMA-D); (2) Greater speaker diversity in CREMA-D (91 actors vs. 10 in EmoDB); (3) Annotation methodology differences—crowd-sourced labeling introduces noise for ambiguous low-arousal categories. See “Analysis of the cross-corpus performance gap” in the Results section. (d) Discuss whether the 30% gap fundamentally limits the “cross-corpus” claim. 2 PONE-D-26-04975 — Response to Reviewers The 68.15% represents meaningful zero-shot generalization across two languages and three recording conditions. High-arousal emotions achieve 71.9%, confirming acoustic universals in f0 and energy. This result matches the 68% reported by FedSER-XAI [9] on the same CREMA-D benchmark. The gap is primarily driven by low-arousal categories—particularly neutral—rather than a fundamental framework limitation. (e) Consider adding t-SNE visualization of feature distributions across datasets. New Fig 9 provides a three-panel t-SNE visualization: (a) dataset-colored view showing domain shift; (b) emotion-colored view revealing cross-dataset clustering for high-arousal emotions; (c) domain shift highlighting CREMA-D relative to training data. See new Fig 9 and accompanying discussion. Comment 2 — Data leakage verification (a) Confirm no speakers appear in both training and test sets. Explicitly confirmed: the global 80/20 split is performed before federated distribution. RAVDESS uses subject-independent splitting; EmoDB uses actor-level stratification. Both guarantee complete speaker disjointness. (b) Details on the “mixed client” composition. Now specified: Client 5 contains 30% EmoDB (∼130 samples) and 70% RAVDESS (∼300 samples), testing cross-lingual heterogeneity. (c) Whether preprocessing was applied globally or per-client. Added: all preprocessing (VAD, normalization, resampling) is applied per-client independently after distribution, preventing cross-client leakage through global statistics. See “Data leakage prevention” paragraph in the Methodology section. Comment 3 — Near-perfect performance scrutiny (a) Report confusion matrix values numerically. New Fig 6 provides clear numerical confusion matrices for all three datasets with large, readable annotations at 300 DPI. (b) Perform ablation with reduced training data to test memorization. New Fig 10 shows monotonic degradation: 88.79% (20%), 93.46% (40%), 96.26% (60%), 98.13% (80%), 99.07% (100%). This rules out memorization—a memorizing model would maintain high accuracy until critical samples were removed, then drop sharply. See new “Near-perfect performance validation” subsection and Fig 10. (c) Compare against human-level performance. Added: EmoDB human recognition ∼84–86% [35]; RAVDESS ∼60–72% [34]. Exceeding human performance is consistent with deep learning advances on acted speech where acoustic patterns are stereotypical. (d) Discuss ceiling effects. EmoDB (535 samples, acted speech) has well-documented ceiling effects with multiple methods >95%. CREMA-D (7,442 samples, 91 actors) is the more meaningful benchmark where 68.15% is well below ceiling. 3 PONE-D-26-04975 — Response to Reviewers Comment 4 — PSO optimization details (a) How was the fitness function computed? Fitness uses 5-fold cross-validation on each client’s local data with a lightweight proxy classifier (single hidden layer, 256 units, 10 epochs). (b) Discuss computational cost of 1,000 model trainings. Using the proxy classifier (∼2.7 sec/evaluation), total PSO time is ∼45 min/client—modest compared to 5.2 h total training. Breakdown in Fig 8(c) and Table 14. (c) Report convergence behavior. New Fig 8 shows: (a) fitness convergence stabilizing by iteration 35; (b) feature count reduction 150→103; (c) cost breakdown. Comments 5–8 — Writing improvements Reduce redundancy, consolidate sections, fix figure ordering, deeper cross-corpus analysis. (1) Abstract rewritten to eliminate redundancy with Introduction and strengthen gap identification; (2) Methodology consolidated into cleaner subsections; (3) All figures referenced sequentially; (4) Cross-corpus analysis substantially expanded as detailed above. Reviewer #2 Major Revision 1 — Multi-scale PSR parameter selection Elaborate on the multi-scale phase space reconstruction methodology and parameter selection rationale. Expanded with: (1) τ = 17 justified via AMI analysis following Fraser & Swinney (1986); (2) d = 3 justified via FNN algorithm; (3) psychoacoustic motivation for the three scales—micro aligns with critical band window, meso with syllabic rate (4–8 Hz), macro with prosodic phrase duration. See “Multi-scale phase space reconstruction” in Methodology. Major Revision 2 — Federated client setup Provide more details on the federated client setup and non-IID data generation process. Now includes: explicit per-client sample counts, mixed client 30/70 ratio, Dirichlet sampling parameters, and per-client preprocessing procedures. Major Revision 3 — Additional datasets Include additional diverse datasets. Adding entirely new datasets would require significant additional experimentation beyond the revision scope. We have instead: (1) strengthened CREMA-D evaluation with per-emotion analysis, t-SNE visualization, and arousal-based transfer analysis; (2) discussed comparability with FedSERXAI results; (3) identified evaluation on additional languages and naturalistic speech as future work. 4 PONE-D-26-04975 — Response to Reviewers Major Revision 4 — Deployment challenges Discuss practical deployment challenges and potential solutions. New “Practical deployment considerations” subsection covers: communication overhead and compression strategies, client heterogeneity and asynchronous aggregation, language diversity limitations, and acted vs. naturalistic speech challenges. Minor Revisions 1–4 Computational efficiency, privacy analysis, statistical validation, consistent notation. (1) Efficiency table expanded with PSO time; (2) Privacy analysis extended with gradient inversion and model inversion attack evaluation; (3) Statistical validation retained with Bonferronicorrected t-tests and supplemented by reduced data ablation; (4) Notation made consistent throughout (scalars italic, vectors bold, matrices bold uppercase). Reviewer #3 Comment 1 — Clarify conclusions with critical analysis Clarify the conclusions better, discuss the results, and support them with critical analysis. The Conclusion has been substantially revised with specific numerical findings, per-emotion cross-corpus summary, discussion of limitations (ceiling effects, neutral variability, acted speech), and concrete future work directions. Comment 2 — Research gap in abstract Clarifying the research gap in the abstract and strengthening it with the datasets used. The abstract has been completely rewritten. It now opens with the research gap (“Current federated learning approaches for SER remain limited...lacking formal privacy guarantees”), names all three datasets, and includes the per-emotion arousal-based analysis as a key finding. Comment 3 — Related work comparison table A table should be provided summarizing the relevant works in terms of their strengths and weaknesses. New Table 1 compares eight key methods across: Key Approach, Strengths, Limitations, and FedEmoNet Advances. This clearly maps how our framework addresses each identified gap. See Table 1 in the Related Work section. Comment 4 — XAI methods comparison Explaining the difference between the XAI methods presented. Added: (1) New Table 5 comparing LIME and SHAP across scope, theoretical basis, faithfulness, cost, and role in FedEmoNet; (2) New Fig 5 directly comparing SHAP and LIME attributions for the same sample (r = 0.997 agreement); (3) Expanded text explaining when and why each method is used. See Table 5, Fig 5, and “Explainable AI framework” subsection. Attachments Attachment Submitted filename: response_to_reviewers.pdf https://doi.org/10.1371/journal.pone.0342953.r002
20 Apr 2026 Decision Letter - Viacheslav Kovtun, Editor Multi-Scale Federated Learning with Differential Privacy: A TCN-Transformer Framework for Cross-Corpus Speech Emotion Recognition PONE-D-26-04975R1 Dear Dr. TAWFIK, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support. 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, Viacheslav Kovtun, Dr.Sc., Ph.D. 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 #3: (No Response) ******** -->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 #3: (No Response) ****** -->3. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #1: (No Response) Reviewer #3: (No Response) ****** -->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 #3: (No Response) ****** -->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 #3: (No Response) ****** -->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: (No Response) Reviewer #3: (No Response) ****** -->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 #3: No ******** https://doi.org/10.1371/journal.pone.0342953.r003
Formally Accepted
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