Federated LLMs: Private & Collaborative AI
How Federated Learning enables privacy-preserving LLM adaptation
This research explores the integration of Federated Learning with Large Language Models, enabling organizations to collaboratively improve AI while keeping sensitive data private.
- Combines federated learning with LLM fine-tuning to preserve data privacy
- Enables multiple parties to adapt powerful language models without sharing raw data
- Traces the evolution of both LLMs and Federated Learning approaches
- Systematically reviews integration methods for privacy-preserving AI
For security-conscious organizations, this approach offers a pathway to leverage advanced AI capabilities while maintaining strict data privacy requirements—essential in regulated industries or when handling sensitive information.