Privacy preserving technologies for modern agriculture
In modern agriculture, data is a valuable asset, often kept private to both protect knowledge and ensure privacy, while creating barriers to collaboration and innovation.
Traditional machine learning relies on centralized data, making it difficult to ensure security and meet regulations. Yet modern AI systems need diverse, large-scale datasets to develop robust, scalable solutions for regional and global challenges.
Our ScaleAgData project team is tackling these challenges with Federated Learning, enabling collaborative model training across decentralized datasets without compromising privacy.
Our aim: empower agriculture with secure, scalable AI solutions that respect data sovereignty and unlock the power of shared knowledge.
In this 4th ScaleAgData Newsltter:
- Innovation area 6: Privacy preserving technologies for modern technologies
- Federated Learning in our RI Lab (RIL) Soil Health
- User quotes
- Recent News
- Upcoming events