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A Survey of Incentive Mechanism Design for Federated Learning
IEEE Transactions on Emerging Topics in Computing, 2021Federated learning is promising in enabling large-scale machine learning by massive clients without exposing their raw data. It can not only enable the clients to preserve the privacy information, but also achieve high learning performance.
Yufeng Zhan +5 more
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Online Mechanism Design with Predictions
ACM Conference on Economics and Computation, 2023Aiming to overcome some of the limitations of worst-case analysis, the recently proposed framework of "algorithms with predictions" allows algorithms to be augmented with a (possibly erroneous) machine-learned prediction that they can use as a guide.
Eric Balkanski +3 more
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Recent advances in wet adhesives: Adhesion mechanism, design principle and applications
, 2021Achieving strong adhesion between the interfaces of similar and dissimilar materials is highly desirable in various fields. However, the adhesion of common adhesives is diminished and even eliminated upon contact with water, because it prevents direct ...
Chunyan Cui, Wenguang Liu
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Springer Texts in Business and Economics, 2021
Pak-Sing Choi, Felix Munoz-Garcia
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Pak-Sing Choi, Felix Munoz-Garcia
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Incentive Mechanism Design for Federated Learning: Challenges and Opportunities
IEEE Network, 2021Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the orchestration of a parameter server (e.g., service provider), while keeping the training ...
Yufeng Zhan +3 more
semanticscholar +1 more source