Towards Encrypted Large Language Models with FHE
Published:Aug 2, 2023 00:00
•1 min read
•Hugging Face
Analysis
This article likely discusses the application of Fully Homomorphic Encryption (FHE) to Large Language Models (LLMs). The core idea is to enable computations on encrypted data, allowing for privacy-preserving LLM usage. This could involve training, inference, or fine-tuning LLMs without ever decrypting the underlying data. The use of FHE could address privacy concerns related to sensitive data used in LLMs, such as medical records or financial information. The article probably explores the challenges of implementing FHE with LLMs, such as computational overhead and performance limitations, and potential solutions to overcome these hurdles.
Key Takeaways
- •FHE enables computations on encrypted LLM data.
- •This enhances privacy by preventing data decryption during LLM operations.
- •Challenges include computational overhead and performance optimization.
Reference
“The article likely discusses the potential of FHE to revolutionize LLM privacy.”