This research introduces a novel framework for enabling secure cross-cloud collaboration in large language model training while preserving privacy.

• Leverages federated learning to facilitate collaborative training across distributed cloud environments
• Implements advanced cryptographic primitives to safeguard sensitive data during model training
• Employs dynamic model aggregation techniques to optimize security and performance
• Addresses critical data leakage threats in multi-cloud LLM deployments

As organizations increasingly deploy LLMs across multiple cloud providers, this research provides crucial security architecture to protect sensitive data while enabling collaborative model improvement.

Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning