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1 Jul 08, 2026
FEDPROX-KQE: TRUST-WEIGHTED FEDERATED LEARNING FOR MULTI-CLASS INTRUSION DETECTION IN INDUSTRIAL IOT NETWORKS

—Securing Industrial Internet of Things (IIoT) net works against intrusion is fundamentally difficult because edge devices are resource-constrained, sensitive operational data can-not be shared across factory boundaries, and different factories encounter different attack types in practice. Centralised detection architectures fail on all three counts, while standard Federated Learning (FL) approaches treat detection as a binary problem, use aggregation rules that ignore client contribution quality, and do not account for the non-independent and identically distributed (non-IID) data distributions that arise across hetero-geneous deployments. This paper proposes FedProx-KQE, which addresses these limitations within a single framework. A Vari-ational Autoencoder–Long Short-Term Memory (VAE-LSTM) local model is extended with a multi-class classification head to identify individual attack categories rather than collapsing all threats into one class. Training data is partitioned across four factory clients with non-overlapping attack profiles to reflect realistic heterogeneity, and FedProx regularisation is applied to prevent local updates from drifting too far from the global model. Aggregation weights are replaced by a composite trust score that accounts for each client’s local accuracy, data volume, and class coverage. Experiments on the X-IIoTID benchmark under both binary and multi-class settings show that FedProx reduces the impact of data heterogeneity compared to standard Federated Averaging (FedAvg), and a Convolutional Neural Network (CNN) ablation study confirms that model capacity is not the binding constraint in the federated multi-class setting. ...

Authors: Pushpraj Shrivastava, Shweta Chouksey, Priyanka Verma.

2 Jul 08, 2026
The Mediating Roles of Trust and Satisfaction in the Relationship Between Hospital Service Quality and Patient Loyalty Among Elderly Chronic Disease Patients: A Comparative Study of Government and Private Hospitals in Chhattisgarh

influences patient loyalty among elderly chronic disease patients in Chhattisgarh, India, with a particular focus on the mediating roles of patient trust and patient satisfaction. We further examine whether these relational mechanisms differ between government and private hospital settings. A quantitative, descriptive, and explanatory design was employed, and primary data were collected from 628 elderly patients aged 60 years and above who were undergoing treatment for chronic conditions such as diabetes, hypertension, and cardiovascular disorders across selected hospitals. A structured questionnaire using validated five-point Likert scales captured responses on service quality, trust, satisfaction, and loyalty. We conducted confirmatory factor analysis and structural equation modeling using SPSS and AMOS, followed by multi-group analysis to compare ownership types. The results demonstrate that hospital service quality significantly and positively affects patient trust, satisfaction, and loyalty. Patient satisfaction emerged as the strongest direct predictor of loyalty, while trust significantly influenced both satisfaction and loyalty. Moreover, sequential mediation effects were confirmed: trust and satisfaction jointly transmit the influence of service quality onto loyalty outcomes. Notably, ownership based comparisons revealed distinct pathways—trust driven loyalty was more pronounced in government hospitals, whereas satisfaction-driven mechanisms dominated in private hospitals. This research contributes an integrated relational model validated within the underexplored context of elderly chronic care in a mixed healthcare system. The findings offer actionable insights for policymakers and hospital administrators, suggesting that strategies to improve patient loyalty must be tailored to hospital ownership type. The study underscores the importance of trust in public-sector care and satisfaction in private-sector care for enhancing long-term patient relationships. ...

Authors: Pashupatinath Tripathi, Dr. Deepa Bakhshi.

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