BasedAI: A Decentralized Solution for Seamless Integration of Privacy and Performance in Large Language Models

The proliferation of Large Language Models (LLMs) highlights the critical need for frameworks that prioritize data privacy while maintaining computational efficiency. Fully Homomorphic Encryption (FHE) presents itself as a promising solution; however, the computational overhead it adds, especially when combined with the resource demands of LLMs, poses a significant hurdle to balancing privacy and performance in distributed AI systems.

In response to this challenge, in a new paper BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs), Based Labs proposes BasedAI, which offers a decentralized approach that seamlessly integrates FHE with LLMs to uphold data confidentiality without sacrificing performance.

BasedAI operates on a decentralized network primarily comprised of entities known as “Brains,” which serve as distributed containers for specific computational tasks, notably for running modified LLMs. Each Brain can choose the LLM it wants its associated miners and validators to operate. BasedAI empowers any LLM operating on a Brain to become a Zero-Knowledge Large Language Model (ZK-LLM) through a combination of Fully Homomorphic Encryption (FHE) and BasedAI’s quantization process, known as Cerberus Squeezing. This ensures that data remains encrypted throughout processing and delivery.

The Cerberus Squeezing approach optimizes the efficiency and speed of neural network operations on encrypted data by focusing on computational resource allocation within multi-head attention mechanisms. This enables BasedAI miners to process and respond to user prompts with LLMs without decrypting queries or responses. By introducing Cerberus Squeezing, BasedAI significantly mitigates the performance degradation typically associated with quantized functions in current FHE-compliant computing environments, thus enhancing the efficiency of interactions between users, miners, and validators.

Moreover, the BasedAI network fosters a competitive ecosystem with a limited number of Brains, Validators, and Miners. Participants are incentivized through a rewards system, where compensation is tied to their contribution level and network efficiency. This incentivization mechanism, facilitated by $BASED token rewards, encourages all parties to maintain high-performance standards.

In summary, the primary contribution of BasedAI lies in its ability to address the challenge of balancing privacy and performance in complex computations through its peer-to-peer network structure.

The paper BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs) is on arXiv.

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Author: Hecate He | Editor: Chain Zhang

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