Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling

  title={Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling},
  author={Yuxi Zhao and Xiaowen Gong and Shiwen Mao},
Federated learning (FL) has emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML, the labels of training data need to be generated manually by human agents. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior… 

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