AI-driven failure detection is becoming essential in industrial manufacturing systems where conventional diagnostic methods often fall short in reliability and live feedback. This paper presents the integration of a modular artificial intelligence framework adapted to overcome these challenges by enabling intelligent fault monitoring systems. The proposed architecture consists of four integrated layers: a Sensor-Derived Adaptive Envelope Layer (SDAEL) for preprocessing and converting complex input signals; an Intelligent Deviation Mapping Engine (IDME), which utilizes a integration of One-Class Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks for failure detection; a Failure Insight Notification Core (FINC) for real-time fault categorization; and a Federated Learning and Recovery Engine (FLARE) that enables secure, decentralized data updates across interlinked nodes. This framework can enables early fault detection in various manufacturing machine sectors. To validate this approach, an experimental deployment of the framework on a plastic bottle manufacturing machine that operated under changing conditions was conducted. The experimental evaluation showed improved detection accuracy and faster responsiveness compared to conventional diagnostic methods. Also Comparative benchmarking demonstrated that FLARE achieved a higher Precision of 0.80, Recall of 0.85, and an F1-score of 0.85, outperforming isolated LACSF models (F1-score 0.72) and LSTM-autoencoders (F1-score 0.62) by 13 and 23 points, respectively.This highlights the framework’s potential to improve fault diagnostics.
This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi-agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.
Xiaoying Liu, Junhao Zheng, Kechen Zheng et al.· IEEE Transactions on Mobile...· 8 citations
Malicious clients participating in data collection and interaction may launch attacks such as model and data poisoning to degrade the performance of the global model and conceal their electricity theft behaviors. Although existing studies have introduced blockchain technology to achieve decentralization, they still suffer from limited pre-aggregation validation dimensions. To address these issues, this paper proposes a blockchain-based federated learning approach with dual-verification (BFL-DV) for electricity theft detection. In the pre-aggregation stage, a multi-metric reputation-based consensus committee verification strategy is designed, which effectively mitigates the impact of malicious participants. In the post-aggregation stage, a dynamic threshold-based blockchain verification strategy is developed to counter security risks during the transmission process, which can refuse malicious global updates adaptively. Experimental results demonstrate that BFL-DV can accurately reduce the impact of all malicious clients under the data poisoning attack. Notably, across various proportions of malicious clients, the proposed framework achieves an average AUC improvement of 32.68% compared with SOTA methods, demonstrating its consistent performance advantage.
Fanghong Guo, Yaoming Lang, Shengwei Li et al.· IEEE Transactions on Smart G...· 1 citation
Digital twin (DT) technology is becoming a foundational component of next-generation 5G and 6G networks, enabling real-time monitoring, predictive analytics, and collaborative research. However, integrating DTs with big data and AI introduces significant privacy, security, and governance challenges. This article presents a privacy-preserving DT intelligence framework that integrates federated learning (FL) and differential privacy (DP) within a secure, standards-based architecture. The design follows ISO/IEC 27001 for information security management, complies with EU GDPR principles for data minimization and consent, and aligns with 3GPP TS 33.501 security procedures for 5G networks. The architecture combines TLS-secured data ingestion, blockchain-backed audit trails, and policy-driven compliance monitoring to ensure trusted DT operations without centralizing sensitive data. By mapping key threat vectors to corresponding international standards, the framework offers a practical blueprint for secure and interoperable DT deployments. The article concludes with deployment considerations and a roadmap for extending existing standards to support AI-native digital twins in future 6G environments.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
