Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Houda, Z. Abou El; Moudoud, H.; Brik, B.
When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network Article de journal
Dans: IEEE Transactions on Consumer Electronics, vol. 71, no 2, p. 7192–7200, 2025, ISSN: 00983063 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML
@article{abouelhoudaWhenFederatedLearning2025,
title = {When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network},
author = {Z. Abou El Houda and H. Moudoud and B. Brik},
url = {https://www.scopus.com/pages/publications/105002607319?origin=resultslist},
doi = {10.1109/TCE.2025.3559004},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {7192–7200},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.},
keywords = {Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML},
pubstate = {published},
tppubtype = {article}
}
Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.
2.
Zhu, Y.; Falk, T.
WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model Article de journal
Dans: IEEE Journal of Biomedical and Health Informatics, vol. 29, no 9, p. 6353–6365, 2025, ISSN: 21682194 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform
@article{zhuWavRxDiseaseAgnosticGeneralizable2025,
title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},
author = {Y. Zhu and T. Falk},
url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},
doi = {10.1109/JBHI.2024.3454550},
issn = {21682194 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {29},
number = {9},
pages = {6353–6365},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},
keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},
pubstate = {published},
tppubtype = {article}
}
Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.
3.
Moudoud, H.; Houda, Z. A. El; Brik, B.; Jan, M. A.; Alshawi, B.
Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems Article de journal
Dans: IEEE Transactions on Consumer Electronics, vol. 71, no 2, p. 6183–6192, 2025, ISSN: 00983063 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system
@article{moudoudAdvancingRobustnessPrivacy2025,
title = {Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems},
author = {H. Moudoud and Z. A. El Houda and B. Brik and M. A. Jan and B. Alshawi},
url = {https://www.scopus.com/pages/publications/105002620945?origin=resultslist},
doi = {10.1109/TCE.2025.3558999},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {6183–6192},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.},
keywords = {Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system},
pubstate = {published},
tppubtype = {article}
}
The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.