federated learning

FedIoMT-RPM v3.0: Lightweight adaptive split federated learning for patient-independent ECG arrhythmia classification

FedIoMT-RPM v3.0: Lightweight adaptive split federated learning for patient-independent ECG arrhythmia classification This release contains the complete code and final results for the revised manuscript submitted to Scientific Reports. It replaces v2.0. Main changes from v2.0 Patient-independent evaluation. All methods...

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

Reliable prediction of the technical condition and remaining useful life of ship power plants (SPPs) remains a challenging task because operating conditions continuously change, degradation mechanisms are highly nonlinear, and sensor measurements are inevitably affected by uncertainty. Although hybrid digital twins (HD...

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 Internet of Things (IoT) has connected billions of physical objects to the internet, enabling smarter homes, manufacturing, critical infrastructure, transportation, and healthcare. However, IoT ecosystems are increasingly vulnerable to cyberattacks due to the growing number of resource-limited devices, weak authent...

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...