IEEE

A Federated Learning-Based Data Augmentation Method for Privacy Preservation Under Heterogeneous Data

Abstract: Federated learning is an important distributed machine learning paradigm. This study proposes a privacy-preserving data augmentation model for federated learning of heterogeneous data, which ...
Journal of Medical Internet Research

Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance

This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics ...
IEEE

Personalized Federated Learning via Gradient-Fusion and Gradient-Decoupling for Heterogeneous Data

Abstract: Federated Learning (FL) enables devices to collaboratively train a global model without centralizing raw data. However, heterogeneous data distributions often hinder a single global model ...