BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs) (2403.01008v1)

Abstract: BasedAI is a distributed network of machines which introduces decentralized infrastructure capable of integrating Fully Homomorphic Encryption (FHE) with any LLM connected to its network. The proposed framework embeds a default mechanism, called "Cerberus Squeezing", into the mining process which enables the transformation of a standard LLMs into encrypted zero-knowledge LLMs, or "ZK-LLMs", leveraging insights from generative adversarial networks for data privacy. This novel quantization mechanism empowers BasedAI miners to process and respond to prompts derived from User interaction with LLMs without the need for decrypting either the queries or their corresponding responses. The introduction of Cerberus Squeezing significantly improves performance degradation caused by quantized functions in current FHE-compliant computing environments by proactively optimizing calls between users, miners, and validators.

• The paper presents BasedAI, a P2P network that integrates Fully Homomorphic Encryption with Zero Knowledge LLMs to ensure privacy-preserving computations.
• The paper details the Cerberus Squeezing technique, which dynamically quantizes computations to overcome traditional FHE performance challenges.
• The paper demonstrates practical applications in healthcare and finance, employing a unique token-based economic model to incentivize network roles.

Unveiling BasedAI: A Paradigm Shift Towards Privacy-Preserving LLMs Through Decentralization

Introduction

In the digital age, data privacy emerges as a paramount concern, especially with the exponential growth of LLMs in critical fields ranging from healthcare to finance. Traditional frameworks often falter at the intersection of maintaining confidentiality and ensuring efficient computation. In response to this conundrum, the paper introduces BasedAI, a pioneering decentralized Peer-to-Peer (P2P) network framework that harmoniously integrates Fully Homomorphic Encryption (FHE) with LLMs to forge a new path for privacy in AI.

BasedAI: A Decentralized Approach to Privacy

BasedAI ushers in a novel architecture predicated on the principles of privacy and efficiency. At its core, BasedAI employs Zero-Knowledge LLMs (ZK-LLMs) via a mechanism dubbed “Cerberus Squeezing,” significantly reducing performance degradation traditionally associated with FHE. This quantization process not only enhances data privacy but also propels the optimization of computational functions within encrypted domains, maintaining data integrity and confidentiality across interactions within the network.

Participant Dynamics in BasedAI Ecosystem

The ecosystem is composed of diverse roles including Brain Owners, Miners, and Validators, each contributing uniquely towards the ecosystem's sustenance and growth. Brain Owners manage the computational tasks, incentivizing Miners and Validators through \$BASED tokens for their computational contributions. This intricately designed economic model fosters a competitive yet cooperative environment, ensuring the network's robustness and efficiency.

The Cerberus Squeezing Technique

At the heart of BasedAI's performance optimization lies the Cerberus Squeezing technique. This innovative approach tailors the FHE mechanism specifically for LLMs, addressing the quintessential challenge of computational burden. By proactively optimizing the computational calls and employing dynamic quantization strategies, Cerberus Squeezing mitigates the traditionally unavoidable performance trade-offs, setting a new standard for executing complex computations under encryption.

Practical Implications and Future Potential

The practical applications of BasedAI's framework extend beyond the theoretical field. For instance, in healthcare, a "Medical Records" Brain could securely process sensitive data, offering invaluable insights without ever compromising patient confidentiality. Similarly, financial analysis, cybersecurity threat intelligence, and anonymous search are areas poised for transformation through BasedAI’s network.

Conclusion and Future Directions

BasedAI epitomizes a revolutionary stride towards reconciling the objectives of data privacy and computational efficiency within the domain of LLMs. Through its decentralized P2P network, innovative use of FHE, and the groundbreaking Cerberus Squeezing technique, it offers a robust platform for privacy-preserving computations. As we look towards the future, the potential expansions and applications of ZK-LLMs promise not only to redefine the landscape of artificial intelligence but also to safeguard the sanctity of personal and sensitive information in an increasingly digital world.