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Shapley Value is commonly adopted to measure and incentivize client participation in federated learning.
On information and sufficiency
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A new metric for probability distributions
Dominik Maria Endres and Johannes E Schindelin · 2003
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Weighted random sampling with a reservoir
Pavlos S Efraimidis and Paul G Spirakis · 2006
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An analysis of single-layer networks in unsupervised feature learning
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Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 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, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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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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Towards taming the resource and data heterogeneity in federated learning
Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, and Yue Cheng · 2019
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Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, and Anuj Kumar · 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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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
Cited alongside, same era.
Rewarding high-quality data via influence functions
Adam Richardson, Aris Filos-Ratsikas, and Boi Faltings · 2019
Cited alongside, same era.
Profit allocation for federated learning
Tianshu Song, Yongxin Tong, and Shuyue Wei · 2019
Cited alongside, same era.
Measure contribution of participants in federated learning
Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Cited alongside, same era.
Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 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
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Fedcoin: A peer-to-peer payment system for federated learning
Yuan Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, and Han Yu · 2020
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Fedfast: Going beyond average for faster training of federated recommender systems
Khalil Muhammad, Qinqin Wang, Diarmuid O’Reilly-Morgan, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor · 2020
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Robust federated learning: The case of affine distribution shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie · 2020
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Budget-bounded incentives for federated learning
Adam Richardson, Aris Filos-Ratsikas, and Boi Faltings · 2020
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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Distributionally robust federated averaging
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 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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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman E. Ozdaglar · 2020
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An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
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Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
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Collaborative machine learning with incentive-aware model rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, and Bryan Kian Hsiang Low · 2020
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Data poisoning attacks against federated learning systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 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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A principled approach to data valuation for federated learning
Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, and Dawn Song · 2020
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Efficient and fair data valuation for horizontal federated learning
Shuyue Wei, Yongxin Tong, Zimu Zhou, and Tianshu Song · 2020
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Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective
Jie Xu and Heqiang Wang · 2020
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Hierarchically fair federated learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, and Mohan Kankanhalli · 2020
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Federated learning on non-iid data silos: An experimental study
Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He · 2021
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