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Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy.
A value for n-person games
Lloyd S Shapley. 1953 · 1953
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Polynomial calculation of the Shapley value based on sampling
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MNIST handwritten digit database
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A survey of trust and reputation management systems in wireless communications
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Profit sharing and efficiency in utility games. In ESA . 43:1–43:14
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General data protection regulation
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A new approximation method for the Shapley value applied to the WTC 9/11 terrorist attack
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Joint service pricing and cooperative relay communication for federated learning. In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) . IEEE, 815–820
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Amirata Ghorbani and James Zou. 2019 · 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 · 2019
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Estimation of Individual Device Contributions for Incentivizing Federated Learning
Takayuki Nishio, Ryoichi Shinkuma, and Narayan B Mandayam. 2020 · 2020
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A Crowdsourcing Framework for On-Device Federated Learning
S. R. Pandey, N. H. Tran, M. Bennis, Y. K. Tun, A. Manzoor, and C. S. Hong. 2020 · 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 · 2020
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Efficient and Fair Data Valuation for Horizontal Federated Learning
Shuyue Wei, Yongxin Tong, Zimu Zhou, and Tianshu Song. 2020 · 2020
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A sustainable incentive scheme for federated learning
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang. 2020 · 2020
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Adam Richardson, Aris Filos-Ratsikas, and Boi Faltings. 2019 · 2019
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Profit Allocation for Federated Learning. In IEEE BigData . 2577–2586
Tianshu Song, Yongxin Tong, and Shuyue Wei. 2019 · 2019
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Measure contribution of participants in federated learning. In IEEE BigData . 2597–2604
Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou. 2019 · 2019
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Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019 · 2019
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Dealing with Label Quality Disparity in Federated Learning
Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, and Zhiqi Shen. 2020 · 2020
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Incentive mechanism design for federated learning with multi-dimensional private information. In WiOPT . 1–8
Ningning Ding, Zhixuan Fang, and Jianwei Huang. 2020 · 2020
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Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, and Kee Siong Ng. 2020b · 2020
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Collaborative fairness in federated learning
Lingjuan Lyu, Xinyi Xu, Qian Wang, and Han Yu. 2020a
Cited in the paper.
Rongfei Zeng, Shixun Zhang, Jiaqi Wang, and Xiaowen Chu. 2020 · 2020
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Hierarchically Fair Federated Learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, and Mohan Kankanhalli. 2020 · 2020
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Towards Personalized Federated Learning
Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang. 2021 · 2021
Closest in time.
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications . 10
Ming Tang and Vincent W S Wong. 2021 · 2021
Closest in time.
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System
Bowen Zhao, Ximeng Liu, and Wei-neng Chen. 2021 · 2021
Closest in time.