Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou · PubMed · 2021
approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies powered by theoretical implications show that, our approach facilitates FL with better generalization performance using fewer communication rounds, compared with the state-of-the-art.