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Fairness has emerged as a critical problem in federated learning (FL).
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Jean-Antoine Désidéri · 2012
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Gradient episodic memory for continuum learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Efficient lifelong learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 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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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2019
Cited alongside, same era.
Learning with long-term remembering: Following the lead of mixed stochastic gradient
Yunhui Guo, Mingrui Liu, Tianbao Yang, and Tajana Rosing · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, and et al · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, and Virginia Smith · 2019
Cited alongside, same era.
Towards fair and decentralized privacy-preserving deep learning with blockchain
Fedmgda+: Federated learning meets multi-objective optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, and Yaoliang Yu · 2020
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Fairness and accuracy in federated learning
Wei Huang, Tianrui Li, Dexian Wang, Shengdong Du, and Junbo Zhang · 2020
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Federated multi-task learning for competing constraints
Tian Li, Shengyuan Hu, Ahmad Beirami, 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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Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, and Jiong Jin · 2019
Cited alongside, same era.
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Mitigating bias in federated learning
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
Cited alongside, same era.
Bandit-based communication-efficient client selection strategies for federated learning
Yae Jee Cho, Samarth Gupta, Gauri Joshi, and Osman Yağan · 2020
Cited alongside, same era.
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
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Collaborative fairness in federated learning
Lingjuan Lyu, Xinyi Xu, and Qian Wang · 2020
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Towards building a robust and fair federated learning system
Xinyi Xu and Lingjuan Lyu · 2020
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Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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