Who to Trust? Aggregating Client Predictions in Federated Distillation.

摘要

Federated Distillation enables distributed learning for clients with heterogeneous model architectures. In this paradigm, the server and clients exchange predictions on a shared unlabeled public dataset, rather than model parameters or gradients.