This research introduces an interactive framework that implements Differential Privacy in federated learning for LLMs while addressing the tradeoff between privacy and model utility.

• Enables organizations to train robust AI models while keeping user data local
• Tackles emerging privacy threats in federated learning environments
• Provides practical implementation beyond theoretical worst-case privacy guarantees
• Demonstrates effectiveness specifically with Large Language Models

The work addresses critical security concerns for enterprises developing AI solutions with sensitive data, offering a pathway to maintain privacy compliance without severely compromising performance.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models