Fedimpro: Measuring and improving client update in federated learning

Z Tang, Y Zhang, S Shi, X Tian, T Liu, B Han… - arXiv preprint arXiv …, 2024 - arxiv.org
Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …
被引用次数：3 相关文章

FedImpro: Measuring and Improving Client Update in Federated Learning

Z Tang, Y Zhang, S Shi, X Tian, T Liu, B Han… - arXiv e …, 2024 - ui.adsabs.harvard.edu
Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …

FedImpro: Measuring and Improving Client Update in Federated Learning

Z Tang, Y Zhang, S Shi, X Tian, T Liu, B Han… - The Twelfth International … - openreview.net
Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …