mlsanityData to 7 Oct 2026

Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges

arXiv 2108.00974 ↗First posted 2 Aug 2021cs.LG cs.CR cs.NIcited by 16

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Abstract (arXiv)

The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including healthcare and transport systems. However, the development of FL-enabled IDS for IoT is in its infancy, and still requires research efforts from various areas, in order to identify the main challenges for the deployment in real-world scenarios. In this direction, our work evaluates a FL-enabled IDS approach based on a multiclass classifier considering different data distributions for the detection of different attacks in an IoT scenario. In particular, we use three different settings that are obtained by partitioning the recent ToNIoT dataset according to IoT devices' IP address and types of attack. Furthermore, we evaluate the impact of different aggregation functions according to such setting by using the recent IBMFL framework as FL implementation. Additionally, we identify a set of challenges and future directions based on the existing literature and the analysis of our evaluation results.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 31 of its 104 references that are in the map. First posted on arXiv 2 Aug 2021.

Builds on

31 of 104 references.

References · 31

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19 more
  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  2. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  3. 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 239
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  5. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
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  11. 2020Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-artcited by 19
  12. 2020Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learningcited by 14
  13. 2021Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacycited by 14
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 4; 2025: 2; 2026: 3.

16 papers.

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  1. 2022Anomaly Detection via Federated Learningcited by 3
  2. 2024FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learningcited by 3
  3. 2023Federated Deep Learning for Intrusion Detection in IoT Networkscited by 2
  4. 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detectioncited by 2
  5. 2023Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantificationcited by 2
  6. 2024Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Modelscited by 2
  7. 2026Balancing Usability and Compliance in AI Smart Devices: A Privacy-by-Design Audit of Google Home, Alexa, and Siricited by 2
  8. 2023A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Securitycited by 1
  9. 2022Detection and Prevention Against Poisoning Attacks in Federated Learningcited by 0
  10. 2023CADeSH: Collaborative Anomaly Detection for Smart Homescited by 0
  11. 2024Enhancing IoT Security Against DDoS Attacks through Federated Learningcited by 0
  12. 2024StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systemscited by 0
4 more
  1. 2025Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selectioncited by 0
  2. 2025Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networkscited by 0
  3. 2026Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Reviewcited by 0
  4. 2026Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updatescited by 0