Digital Twin Framework and Federated Learning for Multi-plant Knowledge Sharing in Decision Support for Electric Steelmaking and Beyond
La Metallurgia Italiana - International Journal of the Italian Association for Metallurgy
The processes of electric steelmaking are complex and difficult to control to achieve sustainable production. To strive towards competitiveness and green transformation, steelmakers apply the Electric Arc Furnace (EAF) to circulate scrap into new products.
Preserving data privacy in Machine Learning pipelines with Federated Learning
FAME's project blog
The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.
Leveraging Large Language Models for Financial Predictions
FAME project blog
In the world of finance, where every decision can have significant ramifications, the possibility of predicting market movements is invaluable. Traditionally, analysts have relied on a combination of data analysis, market trends, and expert insights to make informed predictions.
FAME: Federated Decentralized Trusted Data Marketplace for Embedded Finance
IEEE
Due to its multivariate and multipurpose use and reuse, data’s worth is dramatically increasing, leading to an era characterized by the generation of data marketplaces towards accessing, selling, sharing, and trading data and data assets.