mlsanityData to 7 Oct 2026

Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions

arXiv 2106.09527 ↗First posted 16 Jun 2021cs.CR cs.LGcited by 22

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Abstract (arXiv)

The rapid development of the Internet and smart devices trigger surge in network traffic making its infrastructure more complex and heterogeneous. The predominated usage of mobile phones, wearable devices and autonomous vehicles are examples of distributed networks which generate huge amount of data each and every day. The computational power of these devices have also seen steady progression which has created the need to transmit information, store data locally and drive network computations towards edge devices. Intrusion detection systems play a significant role in ensuring security and privacy of such devices. Machine Learning and Deep Learning with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy. However the privacy and security aspects potentially gets jeopardised due to the need of storing and communicating data to centralized server. On the contrary, federated learning (FL) fits in appropriately as a privacy-preserving decentralized learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system. In order to establish the need for FL, various types of IDS, relevant ML approaches and its associated issues are discussed. The paper presents detailed overview of the implementation of FL in various aspects of anomaly detection. The allied challenges of FL implementations are also identified which provides idea on the scope of future direction of research. The paper finally presents the plausible solutions associated with the identified challenges in FL based intrusion detection system implementation acting as a baseline for prospective research.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 29 of its 152 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

29 of 152 references.

References · 29

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  4. 2019Federated Machine Learning: Concept and Applicationscited by 409
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  7. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  8. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  9. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
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17 more
  1. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  2. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  3. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  4. 2020LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detectioncited by 14
  5. 2021MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentationcited by 14
  6. 2020INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systemscited by 12
  7. 2020FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoTcited by 12
  8. 2019An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learningcited by 9
  9. 2020Evaluating the Communication Efficiency in Federated Learning Algorithmscited by 9
  10. 2020Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Datacited by 9
  11. 2021Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Designcited by 9
  12. 2019Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Thingscited by 7
  13. 2020Federated Mimic Learning for Privacy Preserving Intrusion Detectioncited by 6
  14. 2021Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directionscited by 3
  15. 2017Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Surveycited by 2
  16. 2019Steady-State Simulation for Combined Transmission and Distribution Systemscited by 2
  17. 2021A multiagent based framework secured with layered SVM-based IDS for remote healthcare systemscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 7; 2025: 6; 2026: 3.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  3. 2024On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Casecited by 9
  4. 2024Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelcited by 8
  5. 2024Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networkscited by 5
  6. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  7. 2024On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detectioncited by 4
  8. 2024Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areascited by 4
  9. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  10. 2023Federated Deep Learning for Intrusion Detection in IoT Networkscited by 2
  11. 2025Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detectioncited by 2
  12. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
10 more
  1. 2023A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Securitycited by 1
  2. 2024Celtibero: Robust Layered Aggregation for Federated Learningcited by 1
  3. 2026Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregationcited by 1
  4. 2024StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systemscited by 0
  5. 2025Principles and Components of Federated Learning Architecturescited by 0
  6. 2025WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systemscited by 0
  7. 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
  8. 2025Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learningcited by 0
  9. 2026Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBTcited by 0
  10. 2026PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoTcited by 0