How Federated Learning Enhances AI in the Wine Sector: TITAN Insights from EBDVF 2026

At the 10th European Big Data Value Forum (EBDVF 2026) in Galway, Ireland, the TITAN project had an active presence across both the technical programme and the exhibition area. Consortium partner F6S represented the project on site at the F6S Innovation booth, showcasing TITAN alongside a curated selection of European research initiatives to event delegates.

In parallel, consortium partner ITA (Instituto Tecnológico de Aragón) delivered a dedicated presentation demonstrating how privacy-preserving AI addresses critical data-sharing hurdles in the European AgriTech domain.

Presented by Rafael del Hoyo Alonso and Francisco José Lacueva from ITA’s Big Data and Cognitive Systems team, the talk addressed a common challenge in machine learning: building accurate, reliable models requires diverse datasets, but organisations within the same industry are often hesitant to pool sensitive operational information.

TITAN tackles this issue through two complementary approaches:

• Strengthening EOSC Data Spaces: Adding advanced encryption and anonymisation layers that allow secure data sharing without exposing the origin or details of sensitive commercial assets.
• Federated Learning Frameworks: Keeping raw data on local servers whilst dispatching the training model to each site. Only the learned parameters are combined, ensuring original files never leave their owners’ infrastructure.

Testing Federated Models in Viticulture

This method is currently undergoing real-world validation within the wine sector, an industry facing shifting market demands and the need for greater environmental sustainability.

Using field records from Protected Designations of Origin in Spain (Cariñena, Campo de Borja, and Calatayud) alongside the Vaporrichela estate in Veneto, Italy, the pilot directly compares traditional centralised models with federated learning architectures. The trials focus on two key areas:

1. Predicting vine phenological development stages.
2. Assessing and managing the risk of powdery mildew outbreaks.

This practical AgriTech deployment brings together regional and technical expertise from Regione del Veneto, SARGA (Sociedad Aragonesa de Gestión Agroambiental), ITA, and the University of Murcia. The initiative proves that agricultural producers can develop accurate predictive tools without giving up ownership or control over their data.

For further details on the presentation, read ITA’s original article. Additional context on the architecture is also available in the TITAN AgriTech Newsletter.