FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection
As distributed sensing technologies evolve, the collection of time series data is becoming increasingly decentralized, which introduces serious challenges for both model training and data privacy protection. In response to this trend, federated time series anomaly detection enables collaborative ana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4014 |
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| author | Xiuxian Zhang Hongwei Zhao Weishan Zhang Shaohua Cao Haoyun Sun Baoyu Zhang |
| author_facet | Xiuxian Zhang Hongwei Zhao Weishan Zhang Shaohua Cao Haoyun Sun Baoyu Zhang |
| author_sort | Xiuxian Zhang |
| collection | DOAJ |
| description | As distributed sensing technologies evolve, the collection of time series data is becoming increasingly decentralized, which introduces serious challenges for both model training and data privacy protection. In response to this trend, federated time series anomaly detection enables collaborative analysis across distributed sensing nodes without exposing raw data. However, federated anomaly detection experiences issues with unstable training and poor generalization due to client heterogeneity and the limited expressiveness of single-path detection methods. To address these challenges, this study proposes FedSW-TSAD, a federated time series anomaly detection method based on the Sobolev–Wasserstein GAN (SWGAN). It leverages the Sobolev–Wasserstein constraint to stabilize adversarial training and combines discriminative signals from both reconstruction and prediction modules, thereby improving robustness against diverse anomalies. In addition, FedSW-TSAD adopts a differential privacy mechanism with L2-norm-constrained noise injection, ensuring privacy in model updates under the federated setting. The experimental results determined using four real-world sensor datasets demonstrate that FedSW-TSAD outperforms existing methods by an average of 14.37% in the F1-score while also enhancing gradient privacy under the differential privacy mechanism. This highlights the practical value of FedSW-TSAD for privacy-preserving anomaly detection in sensor-based monitoring systems such as industrial IoT, remote diagnostics, and predictive maintenance. |
| format | Article |
| id | doaj-art-0b14ac57e793456e9f5cd3e1d8f65bb8 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0b14ac57e793456e9f5cd3e1d8f65bb82025-08-20T03:16:50ZengMDPI AGSensors1424-82202025-06-012513401410.3390/s25134014FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly DetectionXiuxian Zhang0Hongwei Zhao1Weishan Zhang2Shaohua Cao3Haoyun Sun4Baoyu Zhang5Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaAs distributed sensing technologies evolve, the collection of time series data is becoming increasingly decentralized, which introduces serious challenges for both model training and data privacy protection. In response to this trend, federated time series anomaly detection enables collaborative analysis across distributed sensing nodes without exposing raw data. However, federated anomaly detection experiences issues with unstable training and poor generalization due to client heterogeneity and the limited expressiveness of single-path detection methods. To address these challenges, this study proposes FedSW-TSAD, a federated time series anomaly detection method based on the Sobolev–Wasserstein GAN (SWGAN). It leverages the Sobolev–Wasserstein constraint to stabilize adversarial training and combines discriminative signals from both reconstruction and prediction modules, thereby improving robustness against diverse anomalies. In addition, FedSW-TSAD adopts a differential privacy mechanism with L2-norm-constrained noise injection, ensuring privacy in model updates under the federated setting. The experimental results determined using four real-world sensor datasets demonstrate that FedSW-TSAD outperforms existing methods by an average of 14.37% in the F1-score while also enhancing gradient privacy under the differential privacy mechanism. This highlights the practical value of FedSW-TSAD for privacy-preserving anomaly detection in sensor-based monitoring systems such as industrial IoT, remote diagnostics, and predictive maintenance.https://www.mdpi.com/1424-8220/25/13/4014anomaly detectionprivacy protectionfederated learning |
| spellingShingle | Xiuxian Zhang Hongwei Zhao Weishan Zhang Shaohua Cao Haoyun Sun Baoyu Zhang FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection Sensors anomaly detection privacy protection federated learning |
| title | FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection |
| title_full | FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection |
| title_fullStr | FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection |
| title_full_unstemmed | FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection |
| title_short | FedSW-TSAD: SWGAN-Based Federated Time Series Anomaly Detection |
| title_sort | fedsw tsad swgan based federated time series anomaly detection |
| topic | anomaly detection privacy protection federated learning |
| url | https://www.mdpi.com/1424-8220/25/13/4014 |
| work_keys_str_mv | AT xiuxianzhang fedswtsadswganbasedfederatedtimeseriesanomalydetection AT hongweizhao fedswtsadswganbasedfederatedtimeseriesanomalydetection AT weishanzhang fedswtsadswganbasedfederatedtimeseriesanomalydetection AT shaohuacao fedswtsadswganbasedfederatedtimeseriesanomalydetection AT haoyunsun fedswtsadswganbasedfederatedtimeseriesanomalydetection AT baoyuzhang fedswtsadswganbasedfederatedtimeseriesanomalydetection |