Federated Learning for Privacy-Preserving Intrusion Detection: A Systematic Review, Taxonomy, Challenges and Future Directions
Subject Areas : Machine learning
Dattatray Raghunath Kale
1
*
,
Swati Shirke-Deshmukh
2
,
Amulkumar Jadhav
3
,
Shrihari Khatawkar
4
,
Sunny Mohite
5
,
Sarang Patil
6
,
Madhav Salunkhe
7
,
Rahul Sonkamble
8
1 - Department of Computer Science & Engineering, MIT Art Design and Technology University, Pune, India
2 - Department of Computer Science & Engineering, Pimpri Chinchwad University, Pune, Maharashtra, India
3 - Department of Computer Science & Engineering, D Y Patil College of Engineering and Technology, Kolhapur, India
4 - Department of Computer Science & Engineering, Annasaheb Dange College of Engineering and Technology, Ashta
5 - D Y Patil College of Engineering and Technology, Kolhapur, India
6 - Amity School of Engineering and Technology, Amity University, Mumbai, Maharashtra, India
7 - Annasaheb Dange College of Engineering and Technology Ashta
8 - Department of Computer Science & Engineering, Pimpri Chinchwad University, Pune, Maharashtra, India
Keywords: Federated Learning, Intrusion Detection, Data Privacy, Cyber security,
Abstract :
This paper presents a systematic review of intrusion detection systems (IDS) that leverage federated learning (FL) to enhance privacy in distributed cybersecurity environments. A total of 78 peer-reviewed studies published between 2019 and 2024 were selected using PRISMA guidelines. We categorize FL-based IDS solutions based on architecture (centralized, decentralized, hierarchical), aggregation methods (e.g., FedAvg, DAFL), and privacy-preserving techniques (e.g., differential privacy, homomorphic encryption). The survey also examines solutions to key challenges such as communication overhead, data heterogeneity, and poisoning attacks. Furthermore, this study outlines unresolved issues and proposes future research directions, including adaptive federated optimization and cross-domain deployments. This review serves as a valuable resource for researchers and practitioners aiming to develop privacy-aware, scalable, and intelligent IDS using federated learning.
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