federated learning

Artificial Intelligence in Dementia with Lewy Bodies — Current Applications, Diagnostic Challenges, and Future Perspectives

Multiodal biomarker studies in mild cognitive impairment with Lewy bodies have suggested that combinations of MRI, EEG, and plasma markers may provide additional prognostic information beyond cognitive testing alone, but these findings should not be interpreted as evidence that AI is ready for autonomous clinical diagn...

Governing Data Flows and AI Innovation in a Fragmented Regulatory Environment

This chapter focuses on the intersectional nature of the essentials of data management privacy laws and strategic artificial intelligence (AI) implementation in intercountry setting. With data emerging as an important catalyst of innovation and competitive advantage organizations are beginning to struggle more to contr...

FedBCFA: a boundary-constrained feature alignment federated learning framework for multi-center breast MRI classification

Multi-center medical image modeling faces persistent challenges arising from data privacy constraints and cross-institutional distribution discrepancies. Federated learning provides a feasible paradigm for collaborative training of intelligent diagnostic models for breast magnetic resonance imaging (MRI) without sharin...

A federated learning and recalibration pipeline for clinical prediction across heterogeneous regions: 30-day mortality after acute myocardial infarction

Privacy-Preserving and Fairness-Aware Secure Aggregation in Federated Learning

Edge-Driven Federated Learning System for Secure and Scalable Financial Data Sharing With Privacy Protection

The results indicate that the proposed edge-driven federated learning framework can support privacy-preserving and robust cross-institutional financial risk modeling and provides an effective solution for collaborative fraud detection and anti-money laundering under data isolation, heterogeneous edge environments, and...

An Explainable Decision Intelligence Architecture for Secure Data Sharing and Risk-Aware Resource Governance

The proposed framework combines hierarchical federated learning with multimodal data analysis, Self-Sovereign Identity, group signatures, differential privacy, SHAP based explanations, Siamese fine-tuning, and Pareto-based optimization.

Mechanical fault diagnosis under long-tailed data: an improved federated learning method integrating attention, asymmetric convolution, and margin calibration

Abstract Due to the inability to collect sufficient fault samples for specific types of gear and bearing faults, the data presents a long-tailed distribution, which hinders the effective construction of neural network diagnostic models. Furthermore, when Federated Learning (FL) methods are introduced to address this lo...

Reproducible simulation code for energy-efficient federated multi-task learning

Version 1.0

Task-Centric Personalized Federated Fine-Tuning of Language Models

Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained on heterogeneous tasks often degrades the overall performance of individual clients. To address this issue, Personalized FL (pFL) aims to cr...

HUNTINGTON'S DISEASE ENRICHED COHORT

This Zenodo record contains a free, evaluation-grade sample of 10,000 synthetic patient records — a genuine, unmodified subset of the full production dataset, not a separate re-generation. It is provided so that researchers, ML engineers, and clinical data scientists can inspect the schema, phenotypic depth, and biomar...

Spatio-Temporal Graph Transformer MARL for Resilient Point of Common Coupling Control in Networked Grid-Tied Micro-grids

The increasing penetration of renewable energy sources introduces significant variability and uncertainty into grid-connected microgrids (MGs), affecting voltage stability, frequency regulation, power quality, fault ride-through capability, and operational resilience at the Point of Common Coupling (PCC). These challen...