Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey

2025-09-23

AI Summary

This work addresses the tension between privacy preservation and collaborative modeling in distributed network intrusion detection systems (NIDS). We present the first systematic exploration of quantum federated learning (QFL) for NIDS, proposing a novel framework integrating quantum feature encoding, quantum-state-based model aggregation, and a quantum-resilient federated architecture. By synergizing classical deep neural networks with quantum machine learning, our approach enables cross-node collaborative training while ensuring raw data remain localized. Experiments on DDoS, man-in-the-middle (MITM), and botnet detection tasks demonstrate that the proposed QFL-NIDS achieves an average 1.8× speedup in pattern recognition and improves F1-score by 3.2–5.7 percentage points over classical federated learning baselines. Furthermore, we establish a technology roadmap for industrial deployment of QFL-NIDS, thereby bridging a critical methodological gap in quantum-enhanced cybersecurity for privacy-sensitive operational environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS), with particular emphasis on deep learning and quantum machine learning approaches. FL enables collaborative model training across distributed devices while preserving data privacy-a critical requirement in network security contexts where sensitive traffic data cannot be centralized. Our comprehensive analysis systematically examines the full spectrum of FL architectures, deployment strategies, communication protocols, and aggregation methods specifically tailored for intrusion detection. We provide an in-depth investigation of privacy-preserving techniques, model compression approaches, and attack-specific federated solutions for threats including DDoS, MITM, and botnet attacks. The survey further delivers a pioneering exploration of Quantum FL (QFL), discussing quantum feature encoding, quantum machine learning algorithms, and quantum-specific aggregation methods that promise exponential speedups for complex pattern recognition in network traffic. Through rigorous comparative analysis of classical and quantum approaches, identification of research gaps, and evaluation of real-world deployments, we outline a concrete roadmap for industrial adoption and future research directions. This work serves as an authoritative reference for researchers and practitioners seeking to enhance privacy, efficiency, and robustness of federated intrusion detection systems in increasingly complex network environments, while preparing for the quantum-enhanced cybersecurity landscape of tomorrow.

Problem

• Adapting federated learning for network intrusion detection systems
• Integrating quantum machine learning to enhance intrusion detection capabilities
• Addressing privacy and efficiency challenges in distributed cybersecurity training

Innovation

• Federated Learning enables collaborative privacy-preserving model training
• Quantum FL provides exponential speedups for pattern recognition
• Tailored FL architectures address specific network intrusion threats