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How to ensure fairness is an important topic in federated learning (FL).
Smoothing and differentiation of data by simplified least squares procedures
Abraham Savitzky and Marcel JE Golay · 1964
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A value for n-person games
Lloyd S Shapley · 1997
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Game theory: analysis of conflict
Roger B Myerson · 1997
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Olivier Bousquet and André Elisseeff · 2002
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Rim-one: An open retinal image database for optic nerve evaluation
Francisco Fumero, Silvia Alayón, José L Sanchez, Jose Sigut, and M Gonzalez-Hernandez · 2011
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Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
Geert Litjens, Robert Toth, Wendy van de Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram van Ginneken, Graham Vincent, Gwenael Guillard, Neil Birbeck, Jindang Zhang, et al · 2014
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Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review
Guillaume Lemaître, Robert Martí, Jordi Freixenet, Joan C Vilanova, Paul M Walker, and Fabrice Meriaudeau · 2015
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Nci-proc. ieee-isbi conf. 2013 challenge: Automated segmentation of prostate structures
Bloch Nicholas, Madabhushi Anant, Huisman Henkjan, Freymann John, Kirby Justin, et al · 2015
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A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis
Jayanthi Sivaswamy, S Krishnadas, Arunava Chakravarty, G Joshi, A Syed Tabish, et al · 2015
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 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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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Shira Mitchell, Eric Potash, et al · 2018
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Retinal fundus images for glaucoma analysis: the riga dataset
Ahmed Almazroa, Sami Alodhayb, Essameldin Osman, Eslam Ramadan, Mohammed Hummadi, Mohammed Dlaim, Muhannad Alkatee, Kaamran Raahemifar, and Vasudevan Lakshminarayanan · 2018
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory
Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang · 2019
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Profit allocation for federated learning
Tianshu Song, Yongxin Tong, and Shuyue Wei · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, et al · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, et al · 2020
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J Sheller, Brandon Edwards, G Anthony Reina, Jason Martin, Sarthak Pati, et al · 2020
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A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
Marc Aubreville, Christof A Bertram, Taryn A Donovan, Christian Marzahl, Andreas Maier, and Robert Klopfleisch · 2020
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2020
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Collaborative fairness in federated learning
Lingjuan Lyu, Xinyi Xu, Qian Wang, and Han Yu · 2020
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FedBN: Federated learning on non-IID features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Federated learning for healthcare informatics
Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang · 2021
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Gradient driven rewards to guarantee fairness in collaborative machine learning
Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, and Bryan Kian Hsiang Low · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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Improving fairness via federated learning
Yuchen Zeng, Hongxu Chen, and Kangwook Lee · 2021
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Shuyue Wei, Yongxin Tong, et al · 2020
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Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models
Daniel Yue Zhang, Ziyi Kou, and Dong Wang · 2020
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Distributionally robust federated averaging
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
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A principled approach to data valuation for federated learning
Tianhao Wang, Johannes Rausch, et al · 2020
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A distributional framework for data valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, et al · 2020
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Wei Du, Depeng Xu, Xintao Wu, and Hanghang Tong · 2021
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Enforcing fairness in private federated learning via the modified method of differential multipliers
Borja Rodríguez Gálvez, Filip Granqvist, Rogier van Dalen, and Matt Seigel · 2021
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Addressing algorithmic disparity and performance inconsistency in federated learning
Sen Cui, Weishen Pan, Jian Liang, et al · 2021
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Fedfair: Training fair models in cross-silo federated learning
Lingyang Chu, Lanjun Wang, Yanjie Dong, et al · 2021
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Data valuation for medical imaging using shapley value and application to a large-scale chest x-ray dataset
Siyi Tang, Amirata Ghorbani, et al · 2021
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, et al · 2021
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Communication-efficient federated learning via knowledge distillation
Chuhan Wu, Fangzhao Wu, et al · 2022
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Federated learning enables big data for rare cancer boundary detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-Han Wang, G Anthony Reina, Patrick Foley, et al · 2022
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Rethinking architecture design for tackling data heterogeneity in federated learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, et al · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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The medical algorithmic audit
Xiaoxuan Liu, Ben Glocker, Melissa M McCradden, Marzyeh Ghassemi, Alastair K Denniston, and Lauren Oakden-Rayner · 2022
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Federated learning meets multi-objective optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, and Yaoliang Yu · 2022
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Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning
Zelei Liu, Yuanyuan Chen, Han Yu, et al · 2022
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Fedfaim: A model performance-based fair incentive mechanism for federated learning
Zhuan Shi, Lan Zhang, Zhenyu Yao, et al · 2022
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Towards more efficient data valuation in healthcare federated learning using ensembling
Sourav Kumar, A Lakshminarayanan, et al · 2022
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