Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop …