This research explores Federated Fine-tuning of Large Language Models (FedLLM), enabling organizations to collaboratively improve AI models without sacrificing data privacy.

• Combines the power of Large Language Models with Federated Learning to enable privacy-preserving model adaptation
• Provides a systematic framework for implementing secure, distributed fine-tuning across multiple organizations
• Traces the historical evolution of both technologies and their integration
• Offers practical approaches for applying these techniques in privacy-sensitive domains

This research is particularly valuable for security-focused organizations that need to leverage collective data insights while maintaining strict privacy compliance and protecting sensitive information.

A Survey on Federated Fine-tuning of Large Language Models