Privacy-Preserving Knowledge Editing for LLMs
A federated approach to updating AI models in decentralized environments
FLEKE introduces a novel federated framework for updating knowledge in large language models across multiple organizations without compromising data privacy.
- Eliminates redundant computations when multiple clients update overlapping knowledge
- Preserves privacy through local processing of sensitive information
- Achieves 2.7-3.8× efficiency improvement over centralized approaches
- Maintains comparable editing success rates to non-federated methods
This breakthrough is particularly valuable for healthcare organizations that need to collaboratively update medical knowledge in AI systems while protecting patient data and maintaining regulatory compliance.