Federated Learning for Cybersecurity: A Privacy-Preserving Approach
The growing number of cyber threats and the implementation of stringent privacy regulations have revealed significant shortcomings in traditional centralized machine learning models, especially in distributed systems like the Internet of Things (IoT). This study presents a Federated Learning (FL) fr...
Saved in:
| Main Authors: | Edi Marian Timofte, Mihai Dimian, Adrian Graur, Alin Dan Potorac, Doru Balan, Ionut Croitoru, Daniel-Florin Hrițcan, Marcel Pușcașu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6878 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Securing IoT Devices: A Literature Survey with Experimental Insights into Threat Detection and Mitigation
by: TIMOFTE, E. M., et al.
Published: (2025-06-01) -
Multi-Layered Security Assessment in mHealth Environments: Case Study on Server, Mobile and Wearable Components in the PHGL-COVID Platform
by: Edi Marian Timofte, et al.
Published: (2025-08-01) -
Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection
by: Chandu Gutti, et al.
Published: (2025-01-01) -
Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management
by: Geeta Sandeep Nadella, et al.
Published: (2025-02-01) -
Refining Ethical Reflections in Cybersecurity Policy and Privacy: Insights for Policy Makers
by: Ryma Abassi
Published: (2025-05-01)