Collaborative Model Training

At T-RIZE Labs, we're building an infrastructure that enables competing firms to collaboratively train machine learning (ML) models without sharing underlying data. This unique approach allows each company to process its data locally and contribute to collective intelligence. The result is improved model accuracy, driven by diverse datasets, while maintaining data privacy. This collaboration fosters a positive-sum game, boosting efficiency and innovation across sectors.

This technology is particularly valuable in fields like risk management and anomaly detection, where data sensitivity is paramount. In sectors like construction, gathering sufficient data for accurate ML training is difficult due to the fragmented nature of the industry, with numerous contractors managing small, isolated datasets protected by internal policies and NDAs. While individually insufficient, Distributed Machine Learning (DML) unlocks these silos by aggregating locally processed data to train highly accurate models, benefiting all participants.

DML allows for periodic data extraction without the need for real-time database connections or on-the-spot inferences. For example, a model trained on historical investments from a consortium of firms could be used by T-RIZE to validate listed assets via inference on its own compute resources.

Verifiable Inference for Smart Contracts

Given the limitations of on-chain AI, such as scalability and privacy concerns, T-RIZE leverages mature and reliable off-chain AI infrastructure combined with Zero-Knowledge Proofs (ZKPs) and other safety mechanisms. This ensures verifiable inference while integrating with smart contracts, confirming the integrity of off-chain results on-chain.

DML Model Governance

To ensure models remain accurate and relevant as conditions evolve, T-RIZE has developed an adaptive governance framework. This framework answers critical questions on when and how models should be updated, whether through retraining or architectural adjustments. It also sets protocols for model validation to avoid bias and errors. The governance layer establishes industry standards for model accuracy and defines monetization strategies that fairly compensate data contributors and model trainers.