مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 13 52 2026 2 3 Federated Learning for Privacy-Preserving Intrusion Detection: A Systematic Review, Taxonomy, Challenges and Future Directions Federated Learning for Privacy-Preserving Intrusion Detection: A Systematic Review, Taxonomy, Challenges and Future Directions 333 345 10.66224/jist.45751.13.52.333 en Dattatray Raghunath Kale Swati Shirke-Deshmukh Pimpri Chinchwad University Pune Amulkumar Jadhav D.Y.Patil College of Engineering and Technology, Kolhapur Maharashtra, India Shrihari Khatawkar Annasaheb Dange College of Engineering and Technology, Ashta India Sunny Mohite D Y Patil College of Engineering and Technology, Kolhapur, India Sarang Patil Amity School of Engineering & Technology, Amity University Mumbai Madhav Salunkhe Annasaheb Dange College of Engineering and Technology, Ashta India Rahul Sonkamble Pimpri Chinchwad University Pune 2024 2 8 <p class="Sammary">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.</p> http://jist.ir/fa/Article/Download/45751