Frain to Train

FRAIN introduces a novel decentralized federated learning method designed to enhance robustness and efficiency in asynchronous training environments. Building upon BRAIN's decentralized architecture, FRAIN integrates two innovative strategies:

• FastSync: Quickly approximates the global model using only recent updates, significantly reducing synchronization overhead for newly joining or returning participants.
• SLERP-based Model Merging: Employs spherical linear interpolation to preserve directional information in model parameters, effectively mitigating performance degradation caused by divergent updates or stale contributions.

Explore the detailed methodology and experimental results in the full paper at https://arxiv.org/abs/2505.04223.

Welcome to BRAIN, a pioneering Blockchain-based Reliable AI Network that aims to revolutionize the way AI models are trained and executed, ensuring transparency, reliability, and efficiency.

BRAIN offers a groundbreaking solution that enhances decentralized AI by merging AI with blockchain for secure, transparent, and scalable AI applications, overcoming challenges with large-scale models and ensuring high throughput and reliability even in the presence of Byzantine nodes.

• Two-Phase Transaction Execution: Enhances blockchain performance by allowing pipelining among inference/training transactions and regular transactions.
• Asynchronous Aggregator-Free Federated Learning: Implements an innovative asynchronous federated learning algorithm that does not require a central aggregator, ensuring privacy and scalability.
• Scalable Verification: Employs a verifiable randomly selected committee for scalable verification of inference and training processes, achieving consensus through smart contracts.

For an in-depth exploration of BRAIN, see the detailed research at https://arxiv.org/abs/2305.04062.

BRAIN's ecosystem comprises several projects focused on AI model training and inference evaluation, as well as smart contract development for secure and efficient operations.

• GPT-J-6B LoRA: A project focused on enhancing the GPT-J-6B model for better performance and efficiency.
• BRAIN Inference Evaluation: Evaluates the efficiency and accuracy of AI-driven inferences within the BRAIN network.
• BRAIN Training Evaluation: Assesses the performance of the BRAIN network in training AI models.

• Verifiable Random Functions: Implements VRFs in Solidity for secure and verifiable random �sortition processes.
• Queue: A smart contract for efficient queue management in the BRAIN network.
• Commit-and-Reveal: Facilitates a secure commit-and-reveal scheme for transparent and trustable transactions.