Federated Learning is an emerging distributed collaborative learning paradigm
used by many of applications nowadays. The effectiveness of federated learning
relies on clients' collective efforts and their willingness to contribute local
data. However, due to privacy concerns and the costs of data collection and
model training, clients may not always contribute all the data they possess,
which would negatively affect the performance of the global model. This paper
presents an incentive mechanism that encourages clients to contribute as much
data as they can obtain. Unlike previous incentive mechanisms, our approach
does not monetize data. Instead, we implicitly use model performance as a
reward, i.e., significant contributors are paid off with better models. We
theoretically prove that clients will use as much data as they can possibly
possess to participate in federated learning under certain conditions with our
incentive mechanism

本文介绍了一种激励机制，鼓励客户共享他们所拥有的尽可能多的数据，以提高联合学习的性能，该机制与以前的不同之处在于使用模型性能而不是数据作为激励，我们在一定的条件下理论上证明了客户将使用他们尽可能多的数据参与联合学习