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Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributions diverge from each other.
Multitask learning
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Mime: Mimicking centralized stochastic algorithms in federated learning
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Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Predicting visual exemplars of unseen classes for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, and Fei Sha · 2017
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Hypernetworks
David Ha, Andrew Dai, and Quoc V Le · 2017
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2017
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Communication-efficient learning of deep networks from decentralized data
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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The devil is in the tails: Fine-grained classification in the wild
Grant Van Horn and Pietro Perona · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and Roland Vollgraf · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and X. He · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Classifier and exemplar synthesis for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
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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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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
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Lower bounds and optimal algorithms for personalized federated learning
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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Federated user representation learning
Duc Bui, Kshitiz Malik, Jack Goetz, Honglei Liu, Seungwhan Moon, Anuj Kumar, and Kang G Shin · 2019
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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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Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
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Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 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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Tighter theory for local sgd on identical and heterogeneous data
A Khaled, K Mishchenko, and P Richtárik · 2020
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Survey of personalization techniques for federated learning
V. Kulkarni, Milind Kulkarni, and A. Pant · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 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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From local sgd to local fixed-point methods for federated learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik · 2020
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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
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Fedsplit: An algorithmic framework for fast federated optimization
Reese Pathak and Martin J Wainwright · 2020
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Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 2020
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Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
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Federated learning with class imbalance reduction
Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang, and Hua Qian · 2020
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Identifying and compensating for feature deviation in imbalanced deep learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan, and Wei-Lun Chao · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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Federated accelerated stochastic gradient descent
Honglin Yuan and Tengyu Ma · 2020
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Federated learning using a mixture of experts
Edvin Listo Zec, Olof Mogren, John Martinsson, Leon René Sütfeld, and Daniel Gillblad · 2020
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Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2021
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Personalized cross-silo federated learning on non-iid data
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Consensus control for decentralized deep learning
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