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Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Deep Learning From Noisy Image Labels With Quality Embedding
Jiangchao Yao, Jiajie Wang, Ivor W. Tsang, Ya Zhang, Jun Sun, Chengqi Zhang, and Rui Zhang. 2019 · 1922
Earlier work this paper cites.
Robust Loss Functions under Label Noise for Deep Neural Networks. In AAAI . AAAI Press, 1919–1925
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry. 2017 · 1925
Earlier work this paper cites.
Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1944–1952
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017 · 1952
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.. In Kdd , Vol. 96. 202–207
Ron Kohavi et al · 1996
Earlier work this paper cites.
The MNIST database of handwritten digits
Yann LeCun. 1998 · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011 · 2011
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification. In CVPR . IEEE Computer Society, 2691–2699
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. 2015 · 2015
Earlier work this paper cites.
Tailin Zhou, Jun Zhang, and Danny Tsang. 2022 · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
WebVision Database: Visual Learning and Understanding from Web Data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool. 2017 · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS (Proceedings of Machine Learning Research, Vol. 54) . PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017 · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization. In ICLR . OpenReview.net
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
Earlier work this paper cites.
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
Earlier work this paper cites.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018 · 2018
Earlier work this paper cites.
A Performance Evaluation of Federated Learning Algorithms. In Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning (Rennes, France) (DIDL ’18) . Association for Computing Machinery, New York, NY, USA, 1–8
Adrian Nilsson, Simon Smith, Gregor Ulm, Emil Gustavsson, and Mats Jirstrand. 2018 · 2018
Earlier work this paper cites.
Human Activity Recognition Using Federated Learning. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) . 1103–1111
Konstantin Sozinov, Vladimir Vlassov, and Sarunas Girdzijauskas. 2018 · 2018
Earlier work this paper cites.
Joint Optimization Framework for Learning With Noisy Labels. In CVPR . Computer Vision Foundation / IEEE Computer Society, 5552–5560
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2018 · 2018
Earlier work this paper cites.
mixup: Beyond Empirical Risk Minimization. In ICLR (Poster) . OpenReview.net
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
Earlier work this paper cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu. 2018 · 2018
Earlier work this paper cites.
Unsupervised Label Noise Modeling and Loss Correction. In ICML (Proceedings of Machine Learning Research, Vol. 97) . PMLR, 312–321
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness. 2019 · 2019
Cited alongside, same era.
Robust Inference via Generative Classifiers for Handling Noisy Labels. In ICML (Proceedings of Machine Learning Research, Vol. 97) . PMLR, 3763–3772
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin. 2019 · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 322–330
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. 2019 · 2019
Cited alongside, same era.
Federated Noisy Client Learning
Li Li, Huazhu Fu, Bo Han, Cheng-Zhong Xu, and Ling Shao. 2021 · 2021
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ClusterFL: a similarity-aware federated learning system for human activity recognition. In MobiSys . ACM, 54–66
Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing. 2021 · 2021
Later among the works it cites.
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, and Daniel L. Rubin. 2021 · 2021
Later among the works it cites.
Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure
Jasmeen Randhawa, Michael Chiang, Natalia Porporato, Anmol A Pardeshi, Justin Dredge, Galo Apolo Aroca, Tin A Tun, Joanne HuiMin Quah, Marcus Tan, Risa Higashita, Tin Aung, Rohit Varma, and Benjamin Y Xu. 2021 · 2021
Later among the works it cites.
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Bayesian Nonparametric Federated Learning of Neural Networks. In ICML (Proceedings of Machine Learning Research, Vol. 97) . PMLR, 7252–7261
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni. 2019 · 2019
Cited alongside, same era.
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane. 2020 · 2020
Cited alongside, same era.
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, and Zhiqi Shen. 2020 · 2020
Cited alongside, same era.
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Davide Chicco and Giuseppe Jurman. 2020 · 2020
Cited alongside, same era.
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. 2020 · 2020
Cited alongside, same era.
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K. Warfield, and Ali Gholipour. 2020 · 2020
Cited alongside, same era.
DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In ICLR . OpenReview.net
Junnan Li, Richard Socher, and Steven C. H. Hoi. 2020 · 2020
Cited alongside, same era.
Federated Learning for Open Banking
Guodong Long, Yue Tan, Jing Jiang, and Chengqi Zhang. 2020 · 2020
Cited alongside, same era.
Robust early-learning: Hindering the memorization of noisy labels. In ICLR . OpenReview.net
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. 2021 · 2021
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Federated Learning for Healthcare Informatics
Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter B. Walker, Jiang Bian, and Fei Wang. 2021 · 2021
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Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. In IEEE BigData . IEEE, 1214–1225
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, and Michael W. Mahoney. 2021 · 2021
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FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. In NeurIPS
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, and Mathieu Andreux. 2022 · 2022
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Fed-DR-Filter: Using global data representation to reduce the impact of noisy labels on the performance of federated learning
Shaoming Duan, Chuanyi Liu, Zhengsheng Cao, Xiaopeng Jin, and Peiyi Han. 2022 · 2022
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Robust Federated Learning with Noisy and Heterogeneous Clients. In CVPR . IEEE, 10062–10071
Xiuwen Fang and Mang Ye. 2022 · 2022
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Learning From Noisy Labels With Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. 2022b · 2022
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RobustFed: A Truth Inference Approach for Robust Federated Learning. In CIKM . ACM, 1868–1877
Farnaz Tahmasebian, Jian Lou, and Li Xiong. 2022 · 2022
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FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, and Jing Jiang. 2022 · 2022
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Logit Clipping for Robust Learning against Label Noise
Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, and Yixuan Li. 2022 · 2022
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FedCorr: Multi-Stage Federated Learning for Label Noise Correction. In CVPR . IEEE, 10174–10183
Jingyi Xu, Zihan Chen, Tony Q. S. Quek, and Kai Fong Ernest Chong. 2022 · 2022
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Robust Federated Learning With Noisy Labels
Seunghan Yang, Hyoungseob Park, Junyoung Byun, and Changick Kim. 2022 · 2022
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When Federated Learning Meets Pre-trained Language Models’ Parameter-Efficient Tuning Methods
Zhuo Zhang, Yuanhang Yang, Yong Dai, Lizhen Qu, and Zenglin Xu. 2022b · 2022
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FedNoRo: towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence . 4424–4432
Nannan Wu, Li Yu, Xuefeng Jiang, Kwang-Ting Cheng, and Zengqiang Yan. 2023 · 2023
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FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, and Zenglin Xu. 2023 · 2023
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A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Haobo Zhang, Junyuan Hong, Fan Dong, Steve Drew, Liangjie Xue, and Jiayu Zhou. 2023 · 2023
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