OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
This work addresses energy efficiency and non-IID data issues for IoT security, but it is incremental as it builds on existing federated learning methods with specific optimizations.
The paper tackles the challenge of high energy consumption and data heterogeneity in federated learning for IoT intrusion detection by proposing OptiFLIDS, which uses pruning and customized aggregation to reduce model complexity and energy use while maintaining detection performance on three datasets.
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.