The future of data-driven food systems may not rely on traditional data-sharing models but on federated platforms—a novel approach that enables organizations to collaborate without exposing sensitive data. By leveraging privacy-preserving AI techniques, federated learning allows multiple parties to gain insights from decentralized data, enhancing decision-making in areas like food safety, supply chains, and sustainability.
These recent publications from the 4TU-Redesign program explore the potential of federated platforms in food systems, highlighting their advantages in balancing data privacy, security, and innovation. Could this be the key to unlocking the next generation of AI-driven food solutions?
Read more:
Towards Privacy-Preserving AI in Food Systems – ScienceDirect
Federated Learning for Food Systems – Nature Food
