SecureIoT-FL: A Federated Learning Framework for Privacy-Preserving Real-Time Environmental Monitoring in Industrial IoT Applications
Industrial environments face increasing challenges in achieving accurate environmental monitoring and maintaining data privacy. This paper presents the SecureIoT-FL framework, an innovative integration of federated learning and multi-sensor fusion for real-time, privacy-preserving prediction of envi...
Saved in:
Main Authors: | Montaser N.A. Ramadan, Mohammed A.H. Ali, Shin Yee Khoo, Mohammad Alkhedher |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-02-01
|
Series: | Alexandria Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824015400 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A lightweight privacy-preserving truth discovery mechanism for IoT
by: Yingjie HE, et al.
Published: (2021-05-01) -
Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities
by: Mahmoud Ragab, et al.
Published: (2025-02-01) -
Policy-Based Smart Contracts Management for IoT Privacy Preservation
by: Mohsen Rouached, et al.
Published: (2024-12-01) -
Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments
by: Mohammed A. Aleisa
Published: (2025-01-01) -
AFM-DViT: A framework for IoT-driven medical image analysis
by: Jiacheng Yang
Published: (2025-02-01)