OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT

2025/10/05 by Saida Elouardi, Elouardi, Saida, Mohammed Jouhari +3 · 1 citation
Computer Science · #Network Security and Intrusion Detection #Internet Traffic Analysis and Secure E-voting #Advanced Malware Detection Techniques

paper · pdf · doi:10.48550/arxiv.2510.05180

Abstract

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems (IDS) are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large datasets, but data sharing is often limited due to privacy and security concerns. Federated Learning (FL) presents a promising alternative by enabling collaborative model training without sharing raw data. Despite its advantages, FL still faces key challenges, such as data heterogeneity (non-IID data) and high energy and computation costs, particularly for resource constrained IoT devices. To address these issues, this paper proposes OptiFLIDS, a novel approach that applies pruning techniques during local training to reduce model complexity and energy consumption. It also incorporates a customized aggregation method to better handle pruned models that differ due to non-IID data distributions. Experiments conducted on three recent IoT IDS datasets, TON_IoT, X-IIoTID, and IDSIoT2024, demonstrate that OptiFLIDS maintains strong detection performance while improving energy efficiency, making it well-suited for deployment in real-world IoT environments.

Citations

• EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions
• NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN
• EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums
• A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
• DepGraph: Towards Any Structural Pruning
• FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
• LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing
• Importance Estimation for Neural Network Pruning
• Federated Learning: Strategies for Improving Communication Efficiency
• Learning both Weights and Connections for Efficient Neural Networks

Cited by

• CyberNER: A Harmonized STIX Corpus for Cybersecurity Named Entity Recognition

Related

• FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method
• Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning
• Federated Deep Learning for Intrusion Detection in IoT Networks
• Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks