Robust Federated Learning with Real‐World Noisy Data

Author:

Xu Jingyi,Chen Zihan,Quek Tony Q. S.,Fong Ernest Chong Kai

Publisher

Wiley

Reference37 articles.

1. Durmus Alp EmreAcar YueZhao RamonMatas MatthewMattina PaulWhatmough andVenkateshSaligrama.Federated learning based on dynamic regularization. InInternational Conference on Learning Representations 2020.

2. LaurentAmsaleg OussamaChelly TeddyFuron StéphaneGirard Michael E.Houle Ken‐ichiKawarabayashi andMichaelNett.Estimating local intrinsic dimensionality. InProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pages29–38 2015.

3. EricArazo DiegoOrtego PaulAlbert NoelO'Connor andKevinMcGuinness.Unsupervised label noise modeling and loss correction. InInternational Conference on Machine Learning pages312–321.PMLR 2019.

4. YiqiangChen XiaodongYang XinQin HanYu BiaoChen andZhiqiShen.Focus: Dealing with label quality disparity in federated learning. InInternational Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI(FL‐IJCAI'20) 2020. URL arXivpreprintarXiv:2001.11359.

5. ZihanChen Kai Fong ErnestChong andTony QSQuek.Dynamic attention‐based communication‐efficient federated learning. InInternational Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI (FTL‐IJCAI'2021) 2021. URL arXivpreprintarXiv:2108.05765.

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