@article {10.3844/jcssp.2025.2581.2592,
article_type = {journal},
title = {Privacy-Preserving Deep Federated Learning on the Edge Using Homomorphic Encryption and Secure Multiparty Computation},
author = {Gudur, Noman Aasif and El-Dosuky, Mohamed and Kamel, Sherif},
volume = {21},
number = {11},
year = {2025},
month = {Dec},
pages = {2581-2592},
doi = {10.3844/jcssp.2025.2581.2592},
url = {https://thescipub.com/abstract/jcssp.2025.2581.2592},
abstract = {The increasing volume of consumer data necessitates reliable edge devices for personalized user experiences. Federated Learning (FL) offers a state-of-the-art approach to decentralized machine learning by leveraging data distributed across multiple client devices. However, user data privacy remains vulnerable to corruption through feature heterogeneity and malicious attacks. While several privacy-preserving techniques have been previously implemented, they suffer from implementation constraints and limited robustness against sophisticated attacks. This paper proposes a deep convolutional neural network mechanism that enhances privacy preservation in FL by combining Homomorphic Encryption (HME) and Secure Multiparty Computation (SMC). The proposed approach is validated through model verification on the CIFAR-100 dataset and a healthcare diabetes dataset case study. Results demonstrate that the proposed mechanism outperforms existing privacy protection methods, particularly against backdoor attacks. By ensuring stronger privacy guarantees, this approach facilitates broader adoption of FL technology across privacy-sensitive domains.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}