Debiased Noise Editing on Foundation Models for Fair Medical Image Classification

A novel debiasing strategy that mitigates spurious correlations in medical images through noise editing, enabling fairness-aware image processing for both white-box and black-box foundation model APIs.

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

A novel federated model soup method that optimizes the trade-off between local and global performance through selective interpolation of model parameters, alleviating overfitting and seeking flat minima for improved generalization.