Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.082782
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning

Saadaldeen Rashid Ahmed1,2,3, Fatima Abu Siryeh3, Mohammed Shamar Yadkar3, Oguz Bayat4, Abu Saleh Musa Miah5, Fahmid Al Farid6,7,*, Hezerul Abdul Karim7,*
1 Artificial Intelligence Engineering Department, College of Engineering, Al-Ayen University, Thi-Qar, Iraq
2 Computer Science, Bayan University, Erbil, Kurdistan, Iraq
3 Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
4 Faculty of Engineering, Department of Electrical & Electronics Engineering, Yeditepe University, Istanbul, Turkey
5 Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
6 Faculty of Computer Science and Informatics, Berlin School of Business and Innovation, Karl-Marx-Straße 97-99, Berlin, Germany
7 Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia
* Corresponding Authors: Fahmid Al Farid. Email: fahmid.alfarid@berlinsbi.com; Hezerul Abdul Karim. Email: hezerul@mmu.edu.my
(This article belongs to the Special Issue: Privacy-Preserving AI: Encryption and Differential Privacy)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082782

Received 23 March 2026; Accepted 03 May 2026; Published online 17 September 2026

Abstract

Privacy-preserving federated learning (FL) has emerged as an effective paradigm for collaborative model training across distributed data sources while maintaining data confidentiality. However, protecting sensitive information during model aggregation remains a significant challenge in distributed deep learning environments. This paper introduces GuardML, a secure federated learning framework that integrates a Recurrent Neural Network (RNN) with Hybrid Homomorphic Encryption (HHE) to enable privacy-preserving learning on high-dimensional distributed datasets. In the proposed system, client nodes perform local model training using encrypted data representations, ensuring that raw data remains protected during the learning process. Encrypted model updates are transmitted to a central server, where secure aggregation is performed without exposing individual client information. Experimental evaluation demonstrates that GuardML achieves a classification accuracy of 98.79%, outperforming several federated learning baselines, including KNN (89.27%), SVM (93.38%), and K-Means (82.86%), combined with Differential Privacy (DP) or Secure Multi-Party Computation (SMPC) mechanisms. Although HHE introduces additional encryption overhead (approximately 120 ms) compared with DP-based approaches (60–80 ms), the framework maintains low inference latency (65 ms) and efficient encrypted communication (12.4 MB). The proposed client–server architecture maintains encryption throughout the learning pipeline, mitigating risks such as adversarial inference, model inversion, and membership inference attacks. The study further analyzes the trade-offs among encryption complexity, computational efficiency, and communication cost in secure federated learning systems. The results demonstrate that hybrid homomorphic encryption can provide strong privacy guarantees while maintaining high predictive performance for distributed machine learning tasks. Future work will focus on improving computational efficiency through GPU-accelerated homomorphic encryption, hybrid cryptographic optimization, and model compression techniques to support scalable privacy-preserving AI applications in distributed data environments.

Keywords

Federated learning; hybrid homomorphic encryption; privacy-preserving machine learning; distributed deep learning; secure model aggregation; recurrent neural networks (RNN)

  • 49

    View

  • 12

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link