Shengchao Chen

Federated learning for medical imaging just got a major upgrade, enabling seamless representation transfer across disjoint modalities while preserving patient privacy.

Domain-aware federated learning unlocks high-quality time series foundation models, even when training on highly heterogeneous datasets.

Time series foundation models in federated learning can be significantly improved by representing data as discrete prototypical memories, rather than relying on continuous latent spaces.