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

_Computers, Materials & Continua · Saadaldeen Rashid Ahmed, Fatima Abu Siryeh, Mohammed Shamar Yadkar et al. · 2026_

Canonical: https://diginpl.com/books/paper-cmc-2026-082782
Last updated: 2026-10-02

## Quick facts

- **Author:** Saadaldeen Rashid Ahmed; Fatima Abu Siryeh; Mohammed Shamar Yadkar; Oguz Bayat; Abu Saleh Musa Miah; Fahmid Al Farid; Hezerul Abdul Karim ([Crossref](https://doi.org/10.32604/cmc.2026.082782))
- **Published:** 2026 ([Crossref](https://doi.org/10.32604/cmc.2026.082782))
- **Publisher:** Tech Science Press ([Crossref](https://doi.org/10.32604/cmc.2026.082782))
- **Journal:** Computers, Materials & Continua ([Crossref](https://doi.org/10.32604/cmc.2026.082782))
- **DOI:** 10.32604/cmc.2026.082782 ([Crossref](https://doi.org/10.32604/cmc.2026.082782))

## Questions

**Who wrote GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning?**  
GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning was written by Saadaldeen Rashid Ahmed, Fatima Abu Siryeh, Mohammed Shamar Yadkar, Oguz Bayat.

**When was GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning first published?**  
GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning was first published in 2026.

## Sources

- [Publisher record (DOI, via Crossref)](https://doi.org/10.32604/cmc.2026.082782)

## Licence and attribution

Open-access publication. Read it free at the publisher: https://doi.org/10.32604/cmc.2026.082782.

## Cite this page

"GuardML: A Hybrid Homomorphic Encryption Framework for Privacy-Preserving Federated Deep Learning". DigiNepal. Last updated 2 October 2026. https://diginpl.com/books/paper-cmc-2026-082782. Retrieved 14 November 2026.