Yexuan Fu

DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm in which clients enable collaborative training without compromising private data. However, how to learn a robust global model in the data-heterogeneous and model-heterogeneous FL scenarios is challenging. To add…