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

arXiv: https://arxiv.org/abs/2106.09527 · HTML: https://mlsanity.com/paper/2106.09527/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 152 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Multi-objective Evolutionary Federated Learning](https://mlsanity.com/paper/1812.07478/) (2018)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Time-Correlated Sparsification for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2101.08837/) (2021)
- [LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection](https://mlsanity.com/paper/2002.04902/) (2020)
- [MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation](https://mlsanity.com/paper/2103.13044/) (2021)
- [INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems](https://mlsanity.com/paper/2007.08795/) (2020)
- [FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT](https://mlsanity.com/paper/2012.06150/) (2020)
- [An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning](https://mlsanity.com/paper/1908.08340/) (2019)
- [Evaluating the Communication Efficiency in Federated Learning Algorithms](https://mlsanity.com/paper/2004.02738/) (2020)
- [Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data](https://mlsanity.com/paper/2006.06983/) (2020)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things](https://mlsanity.com/paper/1906.00567/) (2019)
- [Federated Mimic Learning for Privacy Preserving Intrusion Detection](https://mlsanity.com/paper/2012.06974/) (2020)
- [Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions](https://mlsanity.com/paper/2104.13049/) (2021)
- [Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey](https://mlsanity.com/paper/1701.02145/) (2017)
- [Steady-State Simulation for Combined Transmission and Distribution Systems](https://mlsanity.com/paper/1907.12725/) (2019)
- [A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems](https://mlsanity.com/paper/2104.06498/) (2021)

## Built on by (most cited first, 22 of 22)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model](https://mlsanity.com/paper/2409.19390/) (2024)
- [Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks](https://mlsanity.com/paper/2403.02983/) (2024)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2402.10974/) (2024)
- [Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas](https://mlsanity.com/paper/2408.05761/) (2024)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Federated Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2306.02715/) (2023)
- [Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection](https://mlsanity.com/paper/2503.14618/) (2025)
- [Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2506.09674/) (2025)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security](https://mlsanity.com/paper/2312.04135/) (2023)
- [Celtibero: Robust Layered Aggregation for Federated Learning](https://mlsanity.com/paper/2408.14240/) (2024)
- [Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation](https://mlsanity.com/paper/2602.00219/) (2026)
- [StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems](https://mlsanity.com/paper/2405.13062/) (2024)
- [Principles and Components of Federated Learning Architectures](https://mlsanity.com/paper/2502.05273/) (2025)
- [WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems](https://mlsanity.com/paper/2504.04367/) (2025)
- [A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network](https://mlsanity.com/paper/2505.11551/) (2025)
- [Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT](https://mlsanity.com/paper/2602.16098/) (2026)
- [PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT](https://mlsanity.com/paper/2602.17973/) (2026)

## 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.