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Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels.
FOCUS: Dealing with label quality disparity in federated learning
Chen, Y.; Yang, X.; Qin, X.; Yu, H.; Chen, B.; and Shen, Z. 2020 · 2001
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Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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ImageNet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; and Bernstein, M. 2015 · 2015
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
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Robust loss functions under label noise for deep neural networks
Ghosh, A.; Kumar, H.; and Sastry, P. S. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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To trust or not to trust a classifier
Jiang, H.; Kim, B.; Guan, M.; and Gupta, M. 2018 · 2018
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Nlnl: Negative learning for noisy labels
Kim, Y.; Yim, J.; Yun, J.; and Kim, J. 2019 · 2019
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DivideMix: Learning with noisy labels as semi-supervised learning
Li, J.; Socher, R.; and Hoi, S. C. 2019 · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Shu, J.; Xie, Q.; Yi, L.; Zhao, Q.; Zhou, S.; Xu, Z.; and Meng, D. 2019 · 2019
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Bayesian nonparametric federated learning of neural networks
Yurochkin, M.; Agarwal, M.; Ghosh, S.; Greenewald, K.; Hoang, N.; and Khazaeni, Y. 2019 · 2019
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Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A. T. 2020 · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Lin, T.; Kong, L.; Stich, S. U.; and Jaggi, M. 2020 · 2020
Cited alongside, same era.
Does label smoothing mitigate label noise?
Lukasik, M.; Bhojanapalli, S.; Menon, A.; and Kumar, S. 2020 · 2020
Cited alongside, same era.
Robust early-learning: Hindering the memorization of noisy labels
Xia, X.; Liu, T.; Han, B.; Gong, C.; Wang, N.; Ge, Z.; and Chang, Y. 2020 · 2020
Cited alongside, same era.
Federated Learning Based on Dynamic Regularization
Acar, D. A. E.; Zhao, Y.; Navarro, R. M.; Mattina, M.; Whatmough, P. N.; and Saligrama, V. 2021 · 2021
FedRN: Exploiting k-Reliable neighbors towards robust federated learning
Kim, S.; Shin, W.; Jang, S.; Song, H.; and Yun, S.-Y. 2022 · 2022
Later among the works it cites.
A state-of-the-art survey on solving non-IID data in federated learning
Ma, X.; Zhu, J.; Lin, Z.; Chen, S.; and Qin, Y. 2022 · 2022
Later among the works it cites.
Learning from noisy labels with deep neural networks: A survey
Song, H.; Kim, M.; Park, D.; Shin, Y.; and Lee, J.-G. 2022 · 2022
Later among the works it cites.
Virtual homogeneity learning: Defending against data heterogeneity in federated learning
Tang, Z.; Zhang, Y.; Shi, S.; He, X.; Han, B.; and Chu, X. 2022 · 2022
Later among the works it cites.
Fednoil: A simple two-level sampling method for federated learning with noisy labels
Wang, Z.; Zhou, T.; Long, G.; Han, B.; and Jiang, J. 2022 · 2022
Later among the works it cites.
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Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2021 · 2021
Cited alongside, same era.
Client selection for federated learning with label noise
Yang, M.; Qian, H.; Wang, X.; Zhou, Y.; and Zhu, H. 2021 · 2021
Cited alongside, same era.
Jo-SRC: A contrastive approach for combating noisy labels
Yao, Y.; Sun, Z.; Zhang, C.; Shen, F.; Wu, Q.; Zhang, J.; and Tang, Z. 2021 · 2021
Cited alongside, same era.
Unicon: Combating label noise through uniform selection and contrastive learning
Karim, N.; Rizve, M. N.; Rahnavard, N.; Mian, A.; and Shah, M. 2022 · 2022
Cited alongside, same era.
Masking: A new perspective of noisy supervision
Han, B.; Yao, J.; Niu, G.; Zhou, M.; Tsang, I.; Zhang, Y.; and Sugiyama, M. 2018a
Cited in the paper.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B.; Yao, Q.; Yu, X.; Niu, G.; Xu, M.; Hu, W.; Tsang, I.; and Sugiyama, M. 2018b
Cited in the paper.
Fedcorr: Multi-stage federated learning for label noise correction
Xu, J.; Chen, Z.; Quek, T. Q.; and Chong, K. F. E. 2022 · 2022
Later among the works it cites.
Robust federated learning with noisy labels
Yang, S.; Park, H.; Byun, J.; and Kim, C. 2022 · 2022
Later among the works it cites.
Tackling data heterogeneity in federated learning with class prototypes
Dai, Y.; Chen, Z.; Li, J.; Heinecke, S.; Sun, L.; and Xu, R. 2023 · 2023
Closest in time.
FedNoisy: Federated noisy label learning benchmark
Liang, S.; Huang, J.; Zeng, D.; Hong, J.; Zhou, J.; and Xu, Z. 2023 · 2023
Closest in time.
FedFed: Feature distillation against data heterogeneity in federated learning
Yang, Z.; Zhang, Y.; Zheng, Y.; Tian, X.; Peng, H.; Liu, T.; and Han, B. 2023 · 2023
Closest in time.