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Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients.
The Market for “Lemons”: Quality Uncertainty and the Market Mechanism
George A Akerlof · 1970
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Bengt Holmstrom · 1979
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Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach
Lin Gao, Xinbing Wang, Youyun Xu, and Qian Zhang · 2011
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Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing
Yinghao Yu, Jun Zhang, and Khaled Ben Letaief · 2016
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Non-Cash Auction for Spectrum Trading in Cognitive Radio Networks: Contract Theoretical Model With Joint Adverse Selection and Moral Hazard
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Joint Service Pricing and Cooperative Relay Communication for Federated Learning
Shaohan Feng, Dusit Niyato, Ping Wang, Dong In Kim, and Ying-Chang Liang · 2019
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Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, et al · 2019
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Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory
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Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim · 2019
Trading Data For Learning: Incentive Mechanism for On-Device Federated Learning
Rui Hu and Yanmin Gong · 2020
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Quantifying the Generalization Error in Deep Learning in terms of Data Distribution and Neural Network Smoothness
Pengzhan Jin, Lu Lu, Yifa Tang, and George Em Karniadakis · 2020
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Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
Latif U. Khan, Shashi Raj Pandey, Nguyen H. Tran, Walid Saad, Zhu Han, Minh N. H. Nguyen, and Choong Seon Hong · 2020
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Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
Dongdong Ye, Rong Yu, Miao Pan, and Zhu Han · 2020
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Optimal Contract Design for Efficient Federated Learning With Multi-Dimensional Private Information
Ningning Ding, Zhixuan Fang, and Jianwei Huang · 2021
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Motivating Workers in Federated Learning: A Stackelberg Game Perspective
Yunus Sarikaya and Özgür Erçetin · 2019
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Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems
Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb · 2021
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