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.