T-RIZE announces federated AI blueprint in partnership with Flower to revolutionize data privacy and compliance workflows
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Montréal, Canada — June 24, 2025 – T-RIZE has joined the Flower Pilot Program, a three-month program to advance federated learning through real-world deployments, in a significant move toward redefining privacy in workplace AI.
The Montreal-based company will create and deploy a production-ready blueprint that allows enterprises to safely train AI models on dispersed datasets without transmitting or exposing critical data.
The blueprint, which will be released at the end of the program, will show how to fine-tune pre-trained transformer models on tabular datasets like rental records, underwriting data, and identity verification forms using Flower’s federated AI framework and Rizemind, T-RIZE’s open-source blockchain-integrated AI library.
This strategic relationship positions T-RIZE at the forefront of a rapidly growing industry: privacy-preserving artificial intelligence supported by cryptographic and decentralized infrastructure. The roadmap will provide institutions the tools they need to fulfill increased demand for AI systems that are safe, auditable, and consistent with regulatory requirements.
“Enterprises want the benefits of AI, but they also need guarantees that their data is protected and their processes are verifiable,” stated Madani Boukalba, CEO of T-RIZE Group. “We’ve built Rizemind to address that challenge head-on by embedding privacy, traceability, and economic incentives directly into the learning process.”
Solving the Privacy-Performance Trade-Off
Traditionally, AI development involved centralizing massive datasets, generating worries about data leakage, vendor lock-in, and noncompliance with rising privacy legislation such as GDPR, HIPAA, and Canada’s Bill C-27.
Federated learning provides an appealing alternative, allowing AI models to train across decentralized data environments while keeping the raw data intact.
T-RIZE goes a step further with Rizemind, which adds blockchain-based auditability, incentive layers, and on-chain coordination to federated learning processes. This integration records every model contribution on the Rizenet blockchain, with incentives delivered in the $RIZE token—a utility asset used for compute credits, participation prizes, and performance validation.
The blueprint contains:
- Open-source GitHub repository including transformer model code.
- Docker images for quick deployment.
- Checklists and dashboards for schema validation, performance monitoring, and data alignment.
- Built-in encryption, access control, and network isolation satisfy enterprise-grade security standards.
Why Flower and T-RIZE are a Natural Fit
The Flower framework, employed by companies like as Mozilla, Nvidia, MIT, and Banking Circle, is widely recognized as the industry standard for federated AI research and implementation. Its open environment and modular architecture make it perfect for working with blockchain-based solutions such as Rizemind.
Flower and T-RIZE collaborate to bridge the gap between machine learning efficiency and regulatory-grade transparency. The cooperation enables organizations to train stronger models without giving up ownership of their data, while also creating a tokenized framework that aligns incentives for data sources, model contributors, and validators.
“T-RIZE brings something powerful to the table: a verifiable, decentralized way to collaborate on AI,” stated Dimitris Stripelis, a representative for the Flower Pilot Program. “This partnership is about demonstrating how AI can be both private and accountable at scale.”
A Long-Term Vision of Enterprise AI
Beyond the blueprint, T-RIZE’s roadmap includes more Rizemind improvements, such as zero-knowledge machine learning (zkML) for on-chain verification of model correctness, multi-party computing (MPC) to decrease data risk, and dynamic noise addition to protect privacy in sensitive contexts.
T-RIZE, with significant academic links at École de technologie supérieure (ETS) and rising industry interest in tokenized AI systems, is presenting itself as a category leader in safe, blockchain-integrated machine learning infrastructure.
T-RIZE’s plan comes at an ideal time, as regulatory scrutiny and demand for privacy-preserving AI develop.
Media Contact
T-RIZE Communications
Email: [email protected]
Website: www.rizenet.io
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