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Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

arXiv 2007.09712 ↗First posted 19 Jul 2020cs.LG cs.DC stat.MLcited by 56

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Abstract (arXiv)

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-textitk selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use a gradient compression scheme.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2020.

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Citing papers per year: 2021: 9; 2022: 15; 2023: 12; 2024: 10; 2025: 7; 2026: 3.

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  1. 2023FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learningcited by 59
  2. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  3. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  4. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  5. 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
  6. 2024Deep Learning-based Anomaly Detection and Log Analysis for Computer Networkscited by 23
  7. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  8. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  9. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  10. 2022Federated Learning for 5G Base Station Traffic Forecastingcited by 15
  11. 2023Accelerating Hybrid Federated Learning Convergence under Partial Participationcited by 15
  12. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
38 more
  1. 2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
  2. 2024Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noisescited by 13
  3. 2021Federated Variational Learning for Anomaly Detection in Multivariate Time Seriescited by 12
  4. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  5. 2023A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extractioncited by 12
  6. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  7. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  8. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  9. 2021Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Designcited by 9
  10. 2022Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stationscited by 9
  11. 2023Precursor-of-Anomaly Detection for Irregular Time Seriescited by 9
  12. 2021Decentralized Federated Learning Preserves Model and Data Privacycited by 8
  13. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  14. 2022FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systemscited by 6
  15. 2022FedTADBench: Federated Time-Series Anomaly Detection Benchmarkcited by 6
  16. 2022Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networkscited by 5
  17. 2022An Interpretable Federated Learning-based Network Intrusion Detection Frameworkcited by 4
  18. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  19. 2022Cerberus: Exploring Federated Prediction of Security Eventscited by 4
  20. 2023Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimetercited by 4
  21. 2021Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Designcited by 3
  22. 2024Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Datacited by 3
  23. 2025FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalitiescited by 3
  24. 2022Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networkscited by 2
  25. 2022GowFed -- A novel Federated Network Intrusion Detection Systemcited by 2
  26. 2022NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attackscited by 1
  27. 2023CNTS: Cooperative Network for Time Seriescited by 1
  28. 2024FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Frameworkcited by 1
  29. 2024Federated Learning for Tabular Data using TabNet: A Vehicular Use-Casecited by 1
  30. 2024Harnessing Federated Generative Learning for Green and Sustainable Internet of Thingscited by 1
  31. 2024Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Modelscited by 1
  32. 2025Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Seriescited by 1
  33. 2023DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Predictioncited by 0
  34. 2023Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learningcited by 0
  35. 2024Improving Time Series Classification with Representation Soft Label Smoothingcited by 0
  36. 2024Global Outlier Detection in a Federated Learning Setting with Isolation Forestcited by 0
  37. 2024Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysiscited by 0
  38. 2025iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learningcited by 0

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