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

2025-10-05 · Saida Elouardi, Mohammed Jouhari, Anas Motii arxiv

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.

📄 PDF Abstract BibTeX arXiv:2510.05180

Code (0)

등록된 구현이 없습니다.

Tasks

Intrusion Detection Federated Learning

Improving Transferability of Network Intrusion Detection in a Federated Learning Setup

2024-01-07 · Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal

Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior …

Federated Learning Intrusion Detection Network Intrusion Detection

Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning

2020-12-16 · IEEE Open Journal of the Communications Society (Conference version: IJCNN) 2020 12 · Yuwei Sun, Hiroshi Esaki, Hideya Ochiai.

Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic patterns based on a handcrafted dataset of rules. Recently, the application of machine learning in NIDS helps alleviate the enormou…

Federated Learning Intrusion Detection Network Intrusion Detection Personalized Federated Learning

FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm

2025-08-12 · Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal arxiv

Intrusion Detection Systems (IDS) have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors. Recent deep learning based developments have resulted in significant improv…

Network Intrusion Detection Federated Learning

FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles

2025-12-30 · Devika S, Vishnu Hari, Pratik Narang, Tejasvi Alladi 외 arxiv

This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.

Autonomous Vehicles Intrusion Detection Federated Learning

Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning

2023-11-23 · Abhishek Sebastian, Pragna R, Sudhakaran G, Renjith P N 외

Federated learning is a technique of decentralized machine learning. that allows multiple parties to collaborate and learn a shared model without sharing their raw data. Our paper proposes a federated learning framework …

Federated Learning Intrusion Detection Outlier Detection