Neurolite Network

Neurolite Network builds upon the foundations of extensive research from recent years. Existing solutions solve some of the challenges across different domains. Neurolite Network integrates these approaches into a hybrid implementation suited for real-life Web3 applications.

Neurolite Network allows any Web3 company, protocol, DApps and individuals (“Task Publishers”) to seamlessly deploy privacy-preserving federated AI models and learn from the data of thousands of devices and users (“Task Trainers”) in a global learning network. The network primarily encompasses two scenarios:

1. Inference by Publishers: After the model has been successfully trained, the Task Publisher utilizes it for applications such as behavioral analysis and personalized content. Such applications enhance the quality of the products delivered to their users.

2. Inference by Users: After the model has been successfully trained, it is employed by the Task Trainers (usually as a feature within a product of the Task Publisher), for example, user security, trend prediction, collaborative trading, LLMs, etc.

The architecture design of Neurolite Network leverages a hybrid approach where computation-intensive and data-intensive components are off-chain, combined, and integrated with on-chain components for secured decentralization.

Depending on the use case, the system can support synchronous and asynchronous operations.