T-RIZE, a prominent creator of blockchain-powered AI infrastructure, has officially joined the Flower Pilot Program, a three-month project to scale federated learning in real-world settings.
The startup will offer a production-grade blueprint that allows institutions to train transformer models on sensitive rental data while maintaining privacy, auditability, and regulatory compliance.
The initiative, which is supported by Flower's globally known federated AI ecosystem, allows T-RIZE to demonstrate the possibilities of its Rizemind framework, an open-source toolkit that incorporates blockchain verification and economic coordination into distributed AI processes.
A Real-World Approach to Secure and Decentralized AI
T-RIZE's plan will concentrate on tabular data applications like as KYC, underwriting, and due diligence—all areas where data sensitivity and accuracy are critical. The solution combines Flower's federated training infrastructure with Rizemind's on-chain accountability, utilizing blockchain to record training outcomes and assure verifiability among participants.
"This blueprint isn't just theoretical—it's built for operational teams," said Madani Boukalba, CEO of T-RIZE Group. "We're bridging AI and blockchain not for hype, but to solve real enterprise problems, starting with due diligence workflows where privacy and proof matter equally."
The blueprint includes model code on GitHub, Docker images for deployment, schema validation tools, and step-by-step setup and monitoring instructions. It uses encryption, role-based access, and network limitations to meet high security standards.
Token Utility Provides Transparent Training
A crucial innovation is the blueprint's usage of the $RIZE token, T-RIZE's native digital asset. The coin acts as a unit of account for compute utilization, compensates contributors for model training, and stores results on the Rizenet blockchain. This adds economic coordination to AI activities while also providing transparent performance tracking.
According to the business, this strategy lowers the cost of inter-institutional collaboration, eliminates the need for centralized audits, and opens up new income prospects for data providers and validators.
The blueprint's release is carefully timed: as global organizations face AI compliance demands, federated learning solutions that combine privacy and accountability are becoming not just desirable—but also necessary.
A Strategic Collaboration with Broad-Reaching Impact
The Flower ecosystem, which is already being trusted by Mozilla, Owkin, Banking Circle, and MIT, serves as an excellent basis for this collaborative effort. T-RIZE's contribution adds a Web3-native layer of verifiability to federated learning, enabling traceable outcomes and trustless coordination.
Beyond the pilot, T-RIZE intends to expand Rizemind with zero-knowledge machine learning (zkML), multi-party computing (MPC), and dynamic noise injection—techniques designed to improve privacy while meeting future legal needs.
"AI regulation is coming fast, and enterprises need infrastructure that's not just compliant—but provable," Madani said.
Availability and Participation
The blueprint will be provided at the end of the three-month Flower Pilot Program, with early access accessible to universities using rental datasets or compliance-heavy tabular data. Interested parties are encouraged to contact T-RIZE directly or stay updated via Flower's official channels.
About T-RIZE
T-RIZE is a Montreal-based AI and blockchain company focused on privacy-preserving computation and tokenized machine learning. Backed by research at École de Technologie Supérieure (ETS), T-RIZE enables enterprises to unlock the value of sensitive data through decentralized, verifiable AI systems. For more information, visit rizenet.io.
About Flower
Flower, the top open-source federated AI platform, allows safe, decentralized model training and assessment. Flower offers collaborative AI without sacrificing data protection or governance, trusted by researchers and organizations worldwide. To learn more, visit flower.ai.
Media Contact
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