This research addresses critical security risks in Federated Learning for Large Language Models (FedLLM) by implementing a comprehensive safety framework.

• Creates a two-tier safety system using both pre-filtering of harmful training data and Constitutional AI principles
• Demonstrates improved safety measures without significant performance degradation
• Provides a practical approach for preventing harmful content propagation across distributed LLM deployments
• Establishes a foundation for responsible AI deployment in federated learning environments

Why it matters: As LLMs become more distributed, ensuring they cannot be trained on or generate harmful content is essential for secure enterprise deployment. This framework offers a scalable solution to prevent security vulnerabilities in federated learning systems.

Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI