federated learning

Digital Twins in Oncology: From Multimodal Data Integration to Precision Clinical Practice

This narrative review provides radiologists with a balanced, comprehensive overview of digital twin architecture, advanced enabling technologies, current clinical evidence, a practical roadmap for implementation, and a candid appraisal of barriers to adoption.

Information-Theoretic Foundation for Trustworthy Federated Learning

Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...

Federated Learning for Cross-Location Data Collaborative Training

This paper presents a novel approach to collaborative model training leveraging federated learning, specifically designed for scenarios involving geographically dispersed data sources. The core challenge addressed is the 'data island' problem, where valuable data remains siloed due to logistical, regulatory, or competi...

Dynamic Differential Privacy for Federated Learning

This paper presents a novel approach to differential privacy in federated learning, termed Dynamic Differential Privacy (DDP). The core idea is to adapt the privacy budget dynamically, responding to the varying sensitivity of local data. Traditional federated learning frequently utilizes a static privacy budget, which...

基于自适应的联邦学习算法

This paper investigates the challenges and opportunities presented by federated learning in dynamic environments. Traditional federated learning approaches often rely on fixed network topology and data distribution, leading to suboptimal model performance and inefficient data utilization. We propose a novel self-adapti...

Title: Self-Aware Distributed Computing for Algorithmic Discovery

The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...

Distributed Learning with Federated Bayesian Networks

This paper presents a novel approach to distributed learning leveraging Federated Bayesian Networks (FBNs). Traditional federated learning methods struggle with capturing complex, non-independent data distributions, often leading to suboptimal global models. We introduce a framework where multiple devices collaborative...

Title: Self-Aware Distributed Computing for Algorithmic Discovery

The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...

An Intelligent Framework for Secure and Scalable Child Abuse Data Management Using Machine Learning and Blockchain

Child abuse remains a critical global concern requiring efficient, secure, and scalable data management systems to support early detection and intervention. However, existing centralized approaches suffer from data fragmentation, limited interoperability, and significant privacy and security challenges. This paper prop...

Starfish-FL, an Agentic Federated Learning and Analysis Framework

Starfish-FL is a general-purpose federated learning and analysis platform that orchestrates distributed computation across healthcare institutions without exposing patient-level data. Standardized statistical scripts execute locally behind each institution's firewall and only site-level aggregate outputs are shared for...

MedSparseFL: Sparse and Privacy-Preserving Federated Learning for Healthcare Applications

This project provides processed data indexes for reproducing experiments from the MedSparseFL study, a sparse and privacy-preserving federated learning framework for healthcare applications. The datasets include CheXpert and HAM10000. For each dataset, CSV files (chexpert_index.csv and ham10000_index.csv) contain:Image...

Traffic road control and lane allocation framework for intelligent transportation system using federated deep reinforcement learning