federated learning

CHILD LITERACY AT THE RIGHT AGE AFTER MEETING THE 2025 TARGET: WHAT EXPLAINS THE JUMP FROM 59.2% TO 66% AND WHY THE 67% TARGET FOR 2026 REQUIRES A DIFFERENT STRATEGY

In March 2026, the Anísio Teixeira National Institute for Educational Studies and Research announced that 66% of Brazilian public school children completed the 2nd year of elementary school literate in 2025, exceeding by two percentage points the 64% target of the National Commitment for Child Literacy and advancing se...

Federated deep learning for stroke detection: a privacy-preserving, explainable CNN-LSTM framework.

Strict privacy regulations in healthcare limit the centralized sharing of medical imaging data, creating challenges for developing robust and generalizable diagnostic models. Federated learning (FL) offers a privacy-preserving paradigm in which participating institutions collaboratively train a global model without dir...

vantage6

Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is complete...

Robust Asynchronous Federated Learning with Non-Convex Client Objective: Simulations

vantage6

Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is complete...

Ali-Forootani/Robust-Asynchronous-Federated-Learning: v1.0.1

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Artificial Intelligence in Radiology: Methodological Foundations, Clinical Applications, and Emerging Multimodal Models

Abstract Artificial intelligence (AI) is increasingly shaping radiology, by performing tasks ranging from image reconstruction and workflow optimization to image analysis, reporting support, and multimodal clinical decision support. This narrative review provides a structured overview of the methodological foundations...

Adaptive Federated Learning for Sustainable Edge AI: A Lifelong Continual Learning Framework

A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles

The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European Health Data Space (EHDS) principles, IDERHA addresses criti...

Why One Federated Learning Model Does Not Fit All

AI-SAFE FlightNet : A Next-Generation Artificial Intelligence and Automation Framework for Predictive Flight Safety, Aircraft Health Intelligence, and Ground Operations Coordination

Aviation safety improvement remains a continuous engineering challenge because modern aircraft are sensor-dense, software-intensive, and operationally interconnected, while health and operations data often remain fragmented across onboard, maintenance, dispatch, and ground systems. This fragmentation can delay weak-sig...

Evidential Federated Learning for Robust Multimodal Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) at scale faces two coupled obstacles: conversational data is partitioned across organizations under privacy constraints, and emotion labels are costly, leaving most utterances unlabelled. Federated learning addresses the first, but standard aggregation trusts every client upda...