Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Federated Learning with Sharpness-aware Minimisation for Intrusion Detection in Smart Grids

Ming-Wei Li Awais Aziz Senior Member Ieee Dimitrios Pezaros
· 0 citations · 14 references

TL;DR

This study introduces a novel Federated Learning (FL)-based IDS, integrated with Sharpness-Aware Minimisation (SAM), to enhance intrusion detection while preserving data privacy and minimising the attack surface inherent in centralised data transmissions.

Semantic Scholar
View source

Similar papers

Open access Aug 2026

Adaptive Intrusion Detection in WSNs Using Federated Deep Learning and Homomorphic Encryption

This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.

N. Karthick, K. R. Singh · 0 citations
Conference Open access 2026

Enhanced Intrusion Detection in IoT Networks using Federated Learning

The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.

Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al. · 0 citations
#federated learning Open access Sep 2026

Fed fusion-IDS: a privacy-preserving federated intrusion detection system with multi-objective feature optimization and explainable deep learning

FedFusion-IDS is a new privacy-preserving federated framework for intrusion detection based on federated learning, multi-objective feature selection with NSGA-II and SHAP second-level filter, hybrid deep learning architecture with CNN, BiLSTM, and Attention mechanisms, explainability through SHAP and LIME, and zero-day...

S.SaiKishore Reddy, S. S. Santhosh Kumar · 0 citations
Sep 2026

Federated Learning Model for Network Intrusion Detection with Generative Adversarial Network and Explainable AI in Edge Environments

Accelerated growth of the IoT in recent years has been driven by advancements in wireless transmission and processing. Various edge devices have appeared within IoT networks, such as smartphones, smart military equipment, smart cars, and intelligent applications. These applications are gaining popularity as they effect...

Macherla Malleswara Rao · 0 citations
Review Open access Aug 2026

5G Network Intrusion Detection Method Based on Robust Federated Optimization

This review provides a detailed study of intrusion detection systems in 5G networks that are federated learning-based, and how the federated learning-based intrusion detection systems can address the challenges mentioned above.

C.-J. Wang · 0 citations
#federated learning Open access Aug 2026

Privacy-Enhancing Federated Learning Models for Cybersecurity in IoT Networks

Federated Learning is investigated as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead.

Mohammed Ajuji, Y. M. Malgwi, A. Ahmadu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.