Jul 2026· ICST Transactions on Scalable Information Systems· Vol 13· 0 citations· 27 references
TL;DR
This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity.
Abstract
INTRODUCTION: Heterogeneous edge computing creates data islands due to privacy and environmental heterogeneity. Current solutions lack an overall approach.
Objectives
This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity.
Methods
The framework fuses Dynamic Clustering, Adaptive Gradient Transmission, and Data Distribution Alignment. Validation uses simulations against existing technologies.
Results
The proposed method achieves 0.892±0.021 comprehensive performance, 11.1%–13.6% higher than existing technologies. Under 20% network interruption, attenuation is 3.3% vs. 8.6%–12.5%. Data flow reaches 18.6±0.7 MB/s; privacy leakage is 0.8±0.2 bit.
Conclusion
This study provides reliable support for safe, efficient data element circulation, advancing intelligent systems and cybersecurity.
The rise of the Industrial IoT (IIoT) will result in a surge of IIoT devices with high-velocity data streams requiring rapid, real-time analysis of these data streams to power predictive maintenance and assure cybersecurity. Centralized cloud-based approaches to anomaly detection are hindered by their inherent latency...
This paper proposes a federated edge intelligence framework to effectively tackle some of the important issues of privacy, security and scalability in Internet of Things enabled smart systems. An effective approach that combines federated learning with edge intelligence while adding a trust aware aggregation mechanism...
S. Saranya, M. S., S. R et al.· 2026 International Conferenc...· 0 citations
This work introduces a federated learning framework on campus that provides a resource-conscious multi-layer federating strategy that controls the number of devices taking part in the process as well as the frequency at which aggregation is done based on each device’s capability.
C. Reddy, S. Bhargav, G. Thirupathi et al.· International Journal of Ele...· 0 citations
6G edge networks are changing extremely quickly, and will require smart, real-time control of distributed information while keeping user data confidential. Centralized Learning methods will have adverse impacts due to additional processing, additional communication, and additional data exposure. This paper tackles thes...
Priyanka Singh, U. Anand· 2026 7th International Confe...· 0 citations
This study demonstrates the efficacy of the synergy between federated learning and edge computing in IoT security contexts, providing a scalable and privacy-centric solution for anomaly detection across large-scale distributed devices.
Quan Liu, Yuanyuan Feng· Discover Artificial Intellig...· 0 citations
These findings demonstrate that Edge-ZTA provides an efficient, privacy-preserving, and scalable cybersecurity framework capable of mitigating sophisticated multi-stage cyberattacks while satisfying the stringent performance requirements of next-generation Industrial IoT infrastructures.
Ahmed Ramzi Rashid, Zaydon L. Ali, Ahmed Sedeeq Baker Al-doori· Al-Noor Journal of Engineeri...· 0 citations
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