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Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid El Mhamdi, and Julien Stainer · 2017
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Profit sharing and efficiency in utility games
Sreenivas Gollapudi, Debmalya Kollias, and Venetia Pliatsika · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 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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The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Swaroop Rao, Françoise Beaufays, Hubert Augenstein, Chloé Kiddon, and Daniel Ramage · 2018
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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Jack Goetz, Kshitiz Malik, Seungwhan Bui, 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, Shengli Niyato, and Junshan Zhang · 2019
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On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang · 2019
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Profit allocation for federated learning
Tianshu Song, Yongxin Tong, and Shuyue Wei · 2019
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Measure contribution of participants in federated learning
Guan Wang, Charlie Xiaoqian Dang, and Ziye Zhou · 2019
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Interpret federated learning with shapley values
Guan Wang · 2019
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Federated multi-task learning with hierarchical attention for sensor data analytics
Yujing Chen, Yue Ning, Zheng Chai, and Huzefa Rangwala · 2020
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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A survey of data-driven and knowledge-aware explainable AI
Xiao-Hui Li, Caleb Chen Cao, Shenjia Shi, Xun Xue, et al · 2020
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Estimation of individual device contributions for incentivizing federated learning
Takayuki Nishio, Ryoichi Shinkuma, and Narayan B Mandayam · 2020
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Secure federated feature selection for cross-feature federated learning
Fucheng Pan, Dan Meng, Yu Zhang, and Xiaolin Li · 2020
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A crowdsourcing framework for on-device federated learning
Shashi Raj Pandey, Nguyen H Tran, and Choong Seon Hong · 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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Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2020
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A learning-based incentive mechanism for federated learning
Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo · 2020
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Toward understanding the influence of individual clients in federated learning
Yihao Xue, Chaoyue Niu, Shaojie Tang, Fan Wu, and Guihai Chen · 2021
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Federated feature selection for cyber-physical systems of systems
Pietro Cassará, Alberto Gotta, and Lorenzo Valerio · 2022
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An interpretable federated learning-based network intrusion detection framework
Tian Dong, Song Li, Han Qiu, and Jialiang Lu · 2022
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Fair and efficient contribution valuation for vertical federated learning
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P Friedlander, and Yong Zhang · 2022
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Vertical federated learning-based feature selection with non-overlapping sample utilization
Siwei Feng · 2022
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Xiaolin Chen, Shuai Zhou, Kai Yang, Hao Fan, Zejin Feng, Zhong Chen, Hu Wang, and Yongji Wang · 2021
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Dynamic attention-based communication-efficient federated learning
Zihan Chen, Kai Fong Ernest Chong, and Tony QS Quek · 2021
Cited alongside, same era.
Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, and Qiang Yang · 2021
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Interpretable collaborative data analysis on distributed data
Akira Imakura, Yukihiko Inaba, and Tetsuya Sakurai · 2021
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Oort: Efficient federated learning via guided participant selection
Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury · 2021
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Sample-level data selection for federated learning
Anran Li, Lan Zhang, Juntao Tan, and Xiang-Yang Li · 2021
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Privacy-preserving efficient federated-learning model debugging
Anran Li, Lan Zhang, Junhao Wang, Feng Han, and Xiang-Yang Li · 2021
Cited alongside, same era.
FedSDG-FS: Efficient and secure feature selection for vertical federated learning
Anran Li, Hongyi Peng, Lan Zhang, Jiahui Huang, Qing Guo, Han Yu, and Yang Liu · 2022
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Robust aggregation for federated learning by minimum γ \gamma -divergence estimation
Cen-Jhih Li, Pin-Han Huang, Yi-Ting Ma, Hung Hung, and Su-Yun Huang · 2022
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Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning
Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, and Lizhen Cui · 2022
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Contribution-aware federated learning for smart healthcare
Zelei Liu, Yuanyuan Chen, Yansong Zhao, Han Yu, Zaiqing Nie, Qian Xu, and Qiang Yang · 2022
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Privacy-preserving data filtering in federated learning using influence approximation
Ljubomir Rokvic, Panayiotis Danassis, and Boi Faltings · 2022
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Sample selection with deadline control for efficient federated learning on heterogeneous clients
Jaemin Shin, Yuanchun Li, Yunxin Liu, and Sung-Ju Lee · 2022
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Federated optimization of l0-norm regularized sparse learning
Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan, and Jinbo Bi · 2022
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Efficient participant contribution evaluation for horizontal and vertical federated learning
Junhao Wang, Lan Zhang, Anran Li, Xuanke You, and Haoran Cheng · 2022
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Intrinsic performance influence based participant contribution estimation for horizontal federated learning
Lin Zhang, Lixin Fan, Yong Luo, and Ling-Yu Duan · 2022
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Secure shapley value for cross-silo federated learning
Shuyuan Zheng, Yang Cao, and Masatoshi Yoshikawa · 2022
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Efficient training of large-scale industrial fault diagnostic models through federated opportunistic block dropout,
Yuanyuan Chen, Zichen Chen, Sheng Guo, Yansong Zhao, Zelei Liu, Pengcheng Wu, Chengyi Yang, Zengxiang Li, and Han Yu · 2023
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Flames2graph: An interpretable federated multivariate time series classification framework
Raneen Younis, Zahra Ahmadi, Abdul Hakmeh, and Marco Fisichella · 2023
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Federated feature selection for horizontal federated learning in iot networks
Xunzheng Zhang, Alex Mavromatics, Reza Vafeas, and Dimitra Simeonidou · 2023
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