Overview

Rizenet's machine learning stack is designed to break down data silos, enabling organizations with private data to collaborate on training powerful machine learning models. This collaborative approach not only allows for shared insights without compromising data privacy but also opens up new opportunities for entities to monetize their datasets.

The whole is greater than the sum of its parts.

For example, in real estate, builders often operate under strict Non-Disclosure Agreements (NDAs), which limit data sharing. While individual builders may complete only a few projects each year, collectively they generate a vast amount of valuable insights. Rizenet unifies this intelligence, enabling the development of predictive models that benefit the entire sector.

These models can be applied to Real World Assets (RWA), providing decision-makers and smart contracts with insights to enhance or automate decisions related to on-chain RWA tokens.

Key Features

Privacy-Preserving Federated Learning

The stack is built on Federated Learning principles, which transfer trained models instead of raw or aggregated data. This means that the original data stays with its owner at all times, allowing for secure training even on highly sensitive data such as medical information or data under strict NDAs.

Collaborative Ownership and On-Chain Monetization

The models are collectively owned by those who participate in training, with each contributor's share represented on-chain through ERC20 tokens. This ownership structure allows contributors to receive dividends when the model is used, creating a sustainable and transparent revenue model for participating entities.

A Path to Data Monetization

Through Rizenet's stack, entities can safely monetize their datasets by contributing to shared models that grow in value and accuracy as more data is used for training. This approach provides a clear avenue for organizations to generate returns from their otherwise isolated datasets.

How It Works

1. Data Stays Local: Federated learning ensures that data never leaves the owner's premises, enhancing data security and meeting privacy requirements.
2. Model Updates: Entities train models locally, and only the trained parameters (not the data itself) are shared for model aggregation.
3. On-Chain Representation: Contributions are tokenized as ERC20 tokens, giving each contributor a stake in the model.
4. Revenue Sharing: As the model is used, contributors earn dividends, providing a tangible return for their participation.

With Rizenet's machine learning stack, organizations can unlock the value of their data through secure collaboration, shared ownership, and ongoing revenue generation.