厦门大学平台管理系统卢杨--卢杨的个人主页-- FEDIC: FEDERATED LEARNING ON NON-IID AND LONG-TAILED DATA VIA CALIBRATED DISTILLATION

卢杨

访问量：

开通时间：..

最后更新时间：..

当前位置：卢杨的个人主页 >> 科学研究 >> 论文成果

FEDIC: FEDERATED LEARNING ON NON-IID AND LONG-TAILED DATA VIA CALIBRATED DISTILLATION

点击次数：

发表刊物：arXiv

合写作者：卢杨

论文类型：Preprint (PP)

是否译文：否

发表时间：2022-04-30

上一条：Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

下一条：Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features