The field of artificial intelligence (AI) is rapidly evolving, introducing new methods for data protection. Special attention is being paid to federated learning and its compatibility with blockchain technologies.
What is Federated Learning?
Federated learning is a concept that trains AI models on decentralized data. Each participant keeps their information locally, allowing AI to learn without violating privacy. This method is already being applied in large organizations, including hospitals and banks, to optimize processes and enhance security.
Collaboration between Flower and T-RIZE
Flower and T-RIZE are working on a three-month project aimed at creating a real-world plan for integrating AI with blockchain. Their joint effort involves developing the Rizemind package, which includes access restrictions and safe data management. Participation in Flower's pilot program will demonstrate how federated AI can operate alongside blockchain technologies.
Why is it relevant now?
The rapid advancement of AI is matched by the faster pace of regulation. Governments and corporations demand transparency in data handling. Now, as data protection becomes a necessity, the work of initiatives like Flower and T-RIZE is particularly critical. They are setting standards for the secure use of AI, aiming to minimize legal risks and enhance trust.
Federated learning and blockchain are establishing a new standard in AI integration. The technologies being developed by Flower and T-RIZE promise to change approaches to data protection, creating safe frameworks for working with sensitive information.
