federated learning

Artificial intelligence and machine learning in additive manufacturing

The adoption of artificial intelligence (AI) and machine learning (ML) in part with additive manufacturing (AM) is radically changing the functionality of advanced manufacturing technologies. The chapter includes a thorough analysis of the application of AI and ML approaches to some of the most important issues in AM,...

Federated reinforcement learning-based cooperative operation of community microgrids with electric vehicle charging and swapping stations using asymmetric Nash bargaining

Beyond the AI Classroom: Epistemic Judgement Under Machine Advantage in Federated AI-Native Education

Most discussion of artificial intelligence in education treats AI as a tutor, writing assistant, assessment tool or means of personalisation while leaving the underlying educational architecture intact. This conceptual paper develops a different model. It distinguishes substitution AI, which performs an existing learne...

World Scientific Committee for Planet Earth (WSCPE)

World Scientific Committee for Planet Earth (WSCPE) WSCPE proposes a new, standalone, voluntary and federated scientific and ethical coordination body for any technology with significant transboundary, systemic, planetary or intergenerational effects. It seeks verifiable and equitably distributed human progress under r...

Gozie01/ClinicalTrial.sol: Trust CT

Initial archived release of the TRUST-CT code and supporting materials for the federated learning, policy evaluation, security, and audit analyses.

Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance

This repository contains the implementation and experimental code for our research on federated learning for cross-border malaria surveillance in West Africa, addressing the challenge of data heterogeneity while keeping raw records within national boundaries (an architectural property that is consistent with the data-m...

Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance

This repository contains the implementation and experimental code for our research on federated learning for cross-border malaria surveillance in West Africa, addressing the challenge of data heterogeneity while keeping raw records within national boundaries (an architectural property that is consistent with the data-m...

Federated learning for clinical outcome prediction from structured and temporal health data: A systematic review of performance, heterogeneity, privacy, and clinical translation

Retrospective registration of a PRISMA 2020 systematic review of empirical federated learning (FL) studies that predict individual patient clinical outcomes from structured electronic health records, clinical time series, sensor and text data. The review addresses six research questions: FL algorithms and performance r...

FDCTGAN: Client-local CTGAN-augmented federated learning for imbalanced breast cancer classification

Abstract Objective Federated learning enables collaborative medical model development without centralizing patient records; however, client-level class imbalance may reduce sensitivity to malignant cases. This study evaluated FDCTGAN, a client-local CTGAN-augmented federated learning framework for imbalanced breast can...

Federated Learning Enables Privacy-Preserving Cross-Border Malaria Surveillance

This repository contains the implementation and experimental code for our research on federated learning for cross-border malaria surveillance in West Africa, addressing the challenge of data heterogeneity while keeping raw records within national boundaries (an architectural property that is consistent with the data-m...

Privacy-Preserving Artificial Intelligence in Enterprise Applications: The Role of Federated Learning and Differential Privacy in Organizational Data Protection

Enterprise AI systems increasingly run on personal, financial, and clinical records, which leaves organizations balancing analytical value against privacy exposure and regulatory liability. This paper reviews the two privacy-enhancing technologies (PETs) that dominate that balance in practice, federated learning (FL) a...

Privacy-Preserving Artificial Intelligence in Enterprise Applications: The Role of Federated Learning and Differential Privacy in Organizational Data Protection

Enterprise AI systems increasingly run on personal, financial, and clinical records, which leaves organizations balancing analytical value against privacy exposure and regulatory liability. This paper reviews the two privacy-enhancing technologies (PETs) that dominate that balance in practice, federated learning (FL) a...