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

• URL: http://arxiv.org/abs/2510.05180v2
• Date: Mon, 13 Oct 2025 18:48:26 GMT
• Title: OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
• Authors: Saida Elouardi, Mohammed Jouhari, Anas Motii,
• Abstract summary: In IoT environments, effective Intrusion Detection Systems (IDS) are essential for ensuring security.<n>Traditional machine learning-based IDS models typically require large datasets, but data sharing is often limited due to privacy and security concerns.<n>This paper proposes OptiFLIDS, a novel approach that applies pruning techniques during local training to reduce model complexity and energy consumption.
• Score: 0.8258451067861933
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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