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Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein · 2015
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Self-balancing federated learning with global imbalanced data in mobile systems
Moming Duan, Duo Liu, Xianzhang Chen, Renping Liu, Yujuan Tan, and Liang Liang · 2020
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
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M2m: Imbalanced classification via major-to-minor translation
Jaehyung Kim, Jongheon Jeong, and Jinwoo Shin · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Fed-focal loss for imbalanced data classification in federated learning
Dipankar Sarkar, Ankur Narang, and Sumit Rai · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor · 2020
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Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
Liuyu Xiang, Guiguang Ding, and Jungong Han · 2020
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Behavior mimics distribution: Combining individual and group behaviors for federated learning
Hua Huang, Fanhua Shang, Yuanyuan Liu, and Hongying Liu · 2021
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Fedspeech: Federated text-to-speech with continual learning
Ziyue Jiang, Yi Ren, Ming Lei, and Zhou Zhao · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, and Jiashi Feng · 2021
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Addressing class imbalance in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2021
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Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X. Yu · 2021
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Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation
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Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang, and Hua Qian · 2020
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Federated meta-learning for fraudulent credit card detection
Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
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Fedbe: Making bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2021
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Yuhang Zang, Chen Huang, and Chen Change Loy · 2021
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Deep long-tailed learning: A survey
Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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