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
High-capacity satellite network is the cornerstone of future space-air-ground integrated networks. However, the satellite uplink transmissions still face critical challenges, including severe path loss, complex multi-user interference, and payload constraints. Recently, Reconfigurable Intelligent Surfaces (RIS) and Fluid Antenna Systems (FAS) have shown promise for satellite communications through their dynamic signal reconfiguration. This paper proposes a multi-RIS-assisted satellite Compact Ultra-Massive Antenna Array (CUMA) architecture for multi-user satellite uplink transmission. Specifically, we deploy multiple RISs on the terrestrial side to separate interfering Line-of-Sight (LoS) channels via optimized phase shifts, and adopt a CUMA receiver on the satellite to further mitigate interference through FAS port selection. To solve a sum-rate maximization problem, we alternately optimize FAS port selection using a Forward-Backward Greedy Selection (FBGS) algorithm and RIS phase shifts based on Fractional Programming (FP). To the best of our knowledge, this is the first work to jointly optimize multi-RIS and CUMA in a satellite uplink context, where strong LoS and extreme path loss fundamentally distinguish the design from terrestrial counterparts. Simulation results confirm the effectiveness of the proposed architecture across frequency bands. At 6 GHz, our scheme achieves 181% and 32% rate gains over fixed antennas and traditional CUMA schemes, respectively, while the gains also reach 138% and 27% at 26 GHz, illustrating superiority in both interference-limited and noise-limited regimes.
Kai Feng, Runke Fan, Tianheng Xu et al.· IEEE Open Journal of the Com...· 0 citations
Timely anomaly detection in Industrial Internet of Things (IIoT) monitoring requires robust modeling of noisy multivariate sensor streams. Although decomposition-derived residuals provide a potentially useful auxiliary view for multivariate time-series anomaly detection (MTSAD), they are not clean anomaly surrogates in unsupervised settings, as they also contain normal fluctuations, sensor noise, and decomposition artifacts. Therefore, the key challenge is not simply whether residual evidence is useful, but how it can be integrated stably without disturbing the backbone’s native anomaly discrimination process. To address this issue, we propose stable implicit conditioning (SIC), a lightweight and general framework for decomposition-aware unsupervised MTSAD. Instead of reusing residual evidence through explicit downstream intervention, SIC converts compact residual statistics into a bounded sample-dependent channelwise calibration signal for mild representation-level adaptation. When instantiated on the anomaly Transformer (AT), the resulting AT-SIC improves upon the reproduced backbone on four out of five public benchmarks. Additional analyses on decomposition choices, structured disturbances, boundary cases, and cross-backbone transfer show that SIC provides a more reliable evidence utilization path than several intuitive explicit reuse strategies, while introducing only negligible efficiency overhead. These results suggest that decomposition-derived residual evidence is better exploited implicitly than explicitly in this setting.
Guangxia Xu, Zhuo Ye, Xing Huang et al.· IEEE Internet of Things Jour...· 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
Artificial Intelligence-Generated Content (AIGC) has developed rapidly, with Diffusion Models (DMs) gaining wide attention for their superior image generation capabilities. However, the high computational cost of inference limits their deployment in resource-constrained environments such as edge computing. Cloud-edge collaboration is considered a feasible solution to improve inference efficiency, but existing studies have not fully addressed the issue of trust propagation among multiple entities. To address these, we propose a blockchain-aided trusted inference framework for cloud–edge collaborative DMs. Smart contracts are designed to automate image generation task management and ensure traceability throughout the inference process. Leveraging the step-by-step denoising nature of DMs, we introduce a Siamese Network-based Semantic Matching (SNSM) model to identify whether a new task can reuse intermediate results from historical inferences, thereby reducing redundant computation and improving efficiency. We formulate an objective function to minimize total inference latency by jointly considering queuing, transmission, and computation delays, with image quality metrics as constraints. To solve these, we propose Diffusion-Attention integrated Multi-Agent Reinforcement Learning (DAMARL), which dynamically optimizes task partitioning and scheduling between cloud and edge to reduce latency while preserving generation quality. Extensive experiments show that SNSM achieves 85.3% accuracy in reuse decisions, and DAMARL improves average reward by 17.5% $\sim ~35$ % over existing methods, demonstrating the effectiveness of our approach in enhancing DM inference efficiency and performance.
Yu Song, Yin-Lin Ren, Shao-Yong Guo et al.· IEEE Transactions on Cogniti...· 0 citations
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