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Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
[article]
2021
arXiv
pre-print
Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are
arXiv:2111.11850v1
fatcat:24xqnqiqtbh2hdn6lnkdkctqii