Selective Attention Federated Learning (SAFL) offers a breakthrough approach for training language models on sensitive clinical text while preserving privacy.

• Reduces communication bandwidth by selectively fine-tuning only critical transformer layers
• Enhances differential privacy through focused parameter updates
• Achieves comparable performance to full-model training with significantly lower computational costs
• Demonstrates effectiveness on clinical benchmarks including i2b2 and MIMIC-III

Why It Matters: Healthcare organizations can now collaborate on AI development using sensitive patient data without compromising privacy or efficiency, accelerating the deployment of NLP solutions in clinical settings.

Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification