This research introduces Layer-Skipping Federated Learning to efficiently train large language models across healthcare organizations without compromising patient privacy.

• Selectively fine-tunes only specific layers of pre-trained LLMs, reducing computational and communication costs
• Addresses key challenges in healthcare NLP: privacy concerns, communication overhead, and data heterogeneity
• Demonstrates effectiveness on clinical tasks using i2b2 and MIMIC-III datasets
• Maintains model performance while significantly improving training efficiency

This innovation enables healthcare organizations to collaboratively build powerful NLP models for clinical applications while keeping sensitive patient data secure and private.

Federated Learning with Layer Skipping: Efficient Training of Large Language Models for Healthcare NLP