Introduction

To understand Neurolite, first, let's look at the current problems with AI:

• Privacy & Ownership Concerns - Private data is shared with third parties, monetizing data often means compromising privacy and ownership

• Collaboration Challenges - Difficulties to share insights, models, and data between institutions and jurisdictions.

• Misalignment of Interests - Users and devices are not compensated for their data. Data owners and beneficiaries have misaligned interests

• Data Gaps & Entry Barriers - Lack of accessible data, devices, and computing power.

• Computation & Storage Challenges - Massive amounts of data need to be transferred, stored, and processed

• Data Integrity Concerns - Model developers need to trust the integrity of the training data.

Traditional Approach

Now - Let's understand how Decentralized Federated Learning solves it:

• Privacy-Preserving - Private data is used for LOCAL training & inference. Data never leaves the device.

• Alignment of Interests - Users, devices and institutions are rewarded for their contribution.

• Access to Data - Sensitive data is utilized for model training.

Neurolite's Approach

Neurolite.AI: Secur Decentralized Federated Learning Network

Neurolite has all the advantages of Decentralized Federated Learning written above, and also:

• Data Integrity - Multi-layered defense to evaluate contributions and detect malicious inputs and bad players.

• Full Compatibility with Web2 and Web3 - Easy integration of AI models to any product and project.

• Accessible to All - Teams can access models trained by the masses or deploy custom-made models.