Goto

Collaborating Authors

 heterogeneity





FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Y ang

Neural Information Processing Systems

Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting