With the rapid development of the Internet of Things (IoT) and edge computing, the scale and complexity of modern networks have increased significantly, driving the demand for distributed fault diagnosis. Federated learning (FL) effectively addresses the issues of data privacy and dispersion by enabling edge devices to collaboratively train models without sharing raw data. However, existing FL-based fault diagnosis methods still encounter the following challenges. Firstly, static aggregation strategies struggle to balance the contributions of heterogeneous clients dynamically. Secondly, traditional local models are unable to effectively decouple sparse high and low-frequency features in fault signals, thereby limiting the accuracy of fault identification. Finally, the resource constraints of edge devices restrict the deployment of complex diagnostic models. To address these challenges, we propose a federated learning hybrid dynamic weight adjustment method based on delay and model quality, introducing the concept of “accelerated depreciation” in accounting and taxation and the concept of “asset allocation” in economics to improve the communication efficiency of fault diagnosis and reduce the impact of delay differences due to device heterogeneity on the effect of fault diagnosis. Additionally, we propose an adaptive sparse low high frequencies Transformer, introducing a lightweight attention mechanism and an adaptive feature extraction layer, which significantly reduces the computational overhead while maintaining high diagnostic accuracy. The experimental results show that, compared with the most competitive baseline, our method improves the fault diagnosis accuracy by 1% on the Case Western Reserve University Bearing dataset (CWRU), 0.78% on the Xi’an Jiaotong University Gearbox dataset (XJTU), and 0.44% on the Micro service Edge Computing dataset (MICRO).
Jingting Mei, Yang Yang, Celimuge Wu et al.· IEEE Transactions on Cogniti...· 0 citations
Root cause localization is critical for ensuring service reliability in cloud-edge collaborative microservice systems. In practical scenarios, multiple microservice systems are often hybrid-deployed on shared infrastructure, which poses three challenges for existing methods. First, concurrent systems generate substantial metric noise that interferes with anomaly detection. Second, the hierarchical dependencies spanning cloud, edge, and terminal layers cannot be accurately represented in Euclidean space. Third, gateway services that aggregate traffic from multiple systems exhibit amplified anomaly signals, leading to systematic false alarms. To address these issues, we propose confidence gated agent root cause localization (CGARCL), a framework that integrates hyperbolic geometry with confidence gated agent reasoning. CGARCL consists of three components. The direction constrained budgeted anomaly detection method incorporates baseline robust scoring and temporal continuity constraints to extract high-quality candidate anomalous nodes from noisy metrics. The hyperbolic constrained spatio-temporal graph attention network employs Poincar’e ball mapping and center-based topology aggregation to accurately encode hierarchical service dependencies and generate initial root cause rankings. The confidence gated reranking agent is activated when the score gap between the top two candidates is small or the top-ranked node matches a victim-prone pattern. It then performs structured prompt reasoning to suppress false alarms and produce a refined ranking. Experiments on three cloud-edge collaborative microservices datasets demonstrate that CGARCL achieves ACC@1 of 62.1%, 70.6%, and 73.4%, outperforming the second best approach by 19.1%, 11.4%, and 9.8%.
Yechen He, Yang Yang, Lanlan Rui et al.· IEEE Transactions on Cogniti...· 0 citations
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