federated learning

Adaptive uncertainty-aware hybrid digital twin architecture for predictive diagnostics of ship power plants

This study develops a HDT architecture that combines a deterministic physical model, a CNN–LSTM prediction module, and a cognitive simulation model (CSM) within a single diagnostic framework that enhances transparency of maintenance decisions and demonstrates practical feasibility for intelligent SPPs.

Artifical Intelliegence And Machine Learning in Solid Dosage Formulation

Solid dosage forms, tablets, capsules, pellets, and powders, remain the most widely manufactured medicines worldwide, yet their development has long relied on slow, trial-and-error experimentation that contributes to the 10–15 year, multi-billion-dollar cost of bringing a new drug to market. This review examines how ar...

FedEye: Detecting Diabetic Eye Diseases using Federated Deep Learning and Handling Data Heterogeneity with Fedprox Aggregation

Diabetic Retinopathy (DR), Diabetic Macular Edema (DME), and glaucoma are significant ocular diseases that severely impair vision and require early automated detection for effective clinical management, but the traditionally adopted centralised deep learning based detection poses serious privacy problems for the partic...

Cyber Attacks in the Internet of Things (IoT) and Intelligent Defense Mechanisms

The findings of this review show that proactive, cooperative, and self-healing security mechanisms are more effective than traditional reactive approaches in IoT environments and provide recommendations for developing scalable, energy-efficient, and privacy-preserving IoT security frameworks for future research.

TRI-COUPLING SYSTEM EVOLUTION THEORY III: META-EVOLUTION, ECOLOGY LEARNING & SELF-REVISING RESEARCH GRAMMARS AT THE LIMIT

TRI-COUPLING SYSTEM EVOLUTION THEORY III:META-EVOLUTION, ECOLOGY LEARNING & SELF-REVISING RESEARCH GRAMMARS AT THE LIMIT Evolutionary Memory, Rules for Changing Rules, Plural Selection,Research Phylogenies, Unknown Cartography, Human-AI Coevolution,Deep-Time Method Systems, and Successor Handoff Feng Cheng-en (33) x St...

CIVILIZATION EVOLUTIONARY LEARNING, UNKNOWN CARTOGRAPHY & SELF-REVISING FUTURES AT THE LIMIT Meta-Learning Across Lineages, Research Phylogenies, Adaptive Commons, Multi-Speed Residents, Deep-Time Memory, Successor Autonomy, and Handoff

CIVILIZATION EVOLUTIONARY LEARNING, UNKNOWN CARTOGRAPHY & SELF-REVISING FUTURES AT THE LIMIT Meta-Learning Across Lineages, Research Phylogenies, Adaptive Commons,Multi-Speed Residents, Deep-Time Memory, Successor Autonomy, and Handoff Feng Cheng-en (33) x Starli When can a civilization-of-civilizations learn from its...

TRI-COUPLING SYSTEM EVOLUTION THEORY III: META-EVOLUTION, ECOLOGY LEARNING & SELF-REVISING RESEARCH GRAMMARS AT THE LIMIT

TRI-COUPLING SYSTEM EVOLUTION THEORY III:META-EVOLUTION, ECOLOGY LEARNING & SELF-REVISING RESEARCH GRAMMARS AT THE LIMIT Evolutionary Memory, Rules for Changing Rules, Plural Selection,Research Phylogenies, Unknown Cartography, Human-AI Coevolution,Deep-Time Method Systems, and Successor Handoff Feng Cheng-en (33) x St...

Wind turbine drivetrain fault diagnosis and intelligent O&M: a review

As wind turbines evolve toward larger capacities, fleet-level clustering, and operation under complex conditions, fault mechanisms in key drivetrain components show multi-physics coupling and complex evolution, creating a major bottleneck in condition monitoring: models are often constructible but hard to generalize. A...

FedCUS: A Client-Unperceived Selection Strategy for Data Heterogeneity in Federated Learning

Robust Malicious Client Detection in Federated Learning Using Multi-metric Self-organizing Maps and Hassanat Distance

Raw training artifacts for "Benign Exclusion and Consensus-Test Limits in Heterogeneous Federated Learning"

Raw archive behind the manuscript's tables and checkpoint analyses: per-run records of the canonical campaign, the client-update matrices saved at rounds 0, 9 and 29, and the frozen source snapshots that produced them. Code, summary tables and the scripts that regenerate them are in the project repository, https://gith...

Raw training artifacts for "Benign Exclusion and Consensus-Test Limits in Heterogeneous Federated Learning"

Raw archive behind the manuscript's tables and checkpoint analyses: per-run records of the canonical campaign, the client-update matrices saved at rounds 0, 9 and 29, and the frozen source snapshots that produced them. Code, summary tables and the scripts that regenerate them are in the project repository, https://gith...