Technology Overview

Federated AI: Scalable, Secure, User-Sovereign Learning

At the core of Inite's technological infrastructure is a novel approach to AI that we call Federated AI. Unlike traditional centralised AI systems, where data is aggregated and models are trained in a single location, Federated AI distributes the training process across a network of nodes, each holding a portion of the overall dataset.

This architecture offers several key advantages:

- Scalability: By parallelising the training process across many nodes, Federated AI can handle much larger datasets and more complex models than any single machine could support. This allows Inite to leverage the collective computational power of the network to train state-of-the-art AI agents and services.

- Privacy: Under Federated AI, raw data never leaves the originating device or server. Instead, only the model updates are shared, after being encrypted and anonymised. This preserves user privacy and data sovereignty, while still allowing for collaborative learning.

- Robustness: Decentralising the AI training process makes the system more resilient to single points of failure, whether due to technical issues or malicious attacks. If any node goes offline, the network can continue to function and learn uninterrupted.

- Alignment: By keeping data and training local, Federated AI allows for greater customisation and alignment of AI models to individual users' needs and values. Rather than a one-size-fits-all approach, each user can train their own personalised AI agent that reflects their unique preferences and goals.

Under the hood, Inite's Federated AI system leverages advanced techniques such as differential privacy, secure multi-party computation, and homomorphic encryption to ensure the integrity and confidentiality of the learning process. It also uses incentive mechanisms, such as proof-of-learning and model staking, to encourage nodes to contribute high-quality data and compute resources to the network.