This research introduces FedMLLM, a novel approach for fine-tuning multimodal large language models across distributed private data sources without compromising security.

• Addresses multimodal heterogeneity challenges in real-world federated learning scenarios
• Enables training on privacy-sensitive domains while maintaining data confidentiality
• Expands training data scope through federated learning with multiple private data sources
• Enhances practical applications in security-sensitive environments

For security professionals, this research offers a promising framework to leverage distributed private datasets for AI model improvement while maintaining strict privacy controls—a critical advancement for organizations handling sensitive multimodal content.

FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data