Improving Fairness for Data Valuation in Horizontal Federated Learning. (arXiv:2109.09046v3 [cs.LG] UPDATED)

Federated learning is an emerging decentralized machine learning scheme that
allows multiple data owners to work collaboratively while ensuring data
privacy. The success of federated learning depends largely on the participation
of data owners. To sustain and encourage data owners' participation, it is
crucial to fairly evaluate the quality of the data provided by the data owners
and reward them correspondingly. Federated Shapley value, recently proposed by
Wang et al. [Federated Learning, 2020], is a measure for data value under …

arxiv data fairness federated learning learning valuation