优化联邦学习提升物联网入侵检测能效，兼顾精度与节能。
OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
- 局部训练时剪枝模型，降低计算和能耗。
- 在三组真实物联网数据集上保持高检测率，能耗下降显著。
- 适合资源受限的智能设备部署，兼顾隐私与性能。
在智能家居、工业系统等关键物联网场景中，高效的入侵检测系统（IDS）对保障安全至关重要。然而，传统基于机器学习的IDS通常需要大量数据，而隐私与安全顾虑限制了数据共享。联邦学习（FL）通过不共享原始数据即可协同训练模型，成为可行替代方案。但其仍面临数据异构（非独立同分布）及资源受限设备上的高能耗、高计算成本问题。为此，本文提出OptiFLIDS，通过在本地训练阶段引入剪枝技术降低模型复杂度与能耗，并设计定制化聚合方法以应对因非独立同分布数据导致的模型差异。在TON_IoT、X-IIoTID和IDSIoT2024三个近期物联网入侵检测数据集上的实验表明，OptiFLIDS在保持强检测性能的同时显著提升能效，适用于真实物联网环境部署。
原文摘要 · Abstract (English)
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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