This research introduces FedMLLM, a novel approach that fine-tunes multimodal large language models across distributed private datasets without sharing sensitive data.

• Enables training on privacy-sensitive multimodal data across multiple organizations
• Addresses multimodal heterogeneity challenges where different sites have varying data types (images, text, etc.)
• Demonstrates improved security through federated learning that keeps private data local
• Evaluated across multiple domains including security-critical applications

For security professionals, this approach offers a pathway to leverage the power of MLLMs while maintaining strict data privacy requirements and regulatory compliance.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data