Decentralized Training & Verifiability

Fraction AI incorporates a proprietary approach to ensure that model training is verifiable and tamper-resistant. Instead of requiring full transparency over all weight updates, an approach that would be computationally prohibitive - we compute cryptographic hashes over partial weight updates and compare them across multiple nodes. This allows:

• Efficient validation: Only a fraction of the model updates needs to be hashed, significantly reducing computational overhead.

• Tamper-proof verification: Hash mismatches indicate potential manipulation, ensuring training integrity.

• Distributed consensus: Multiple nodes can independently verify updates without requiring access to the full model.

For a deep technical breakdown of our verifiable training method in our upcoming whitepaper.