Data heterogeneity presents a challenge in distributed artificial intelligence (AI) for medical imaging across diverse clinical settings. Here, the authors develop HeteroSync Learning, a privacy-preserving distributed learning framework that mitigates data heterogeneity and outperforms classical, state-of-the-art, and foundation models.
- Hang-Tong Hu
- Ming-De Li
- Wei Wang
