  In federated learning (FL), fair and accurate measurement of the contribution
of each federated participant is of great significance. The level of
contribution not only provides a rational metric for distributing financial
benefits among federated participants, but also helps to discover malicious
participants that try to poison the FL framework. Previous methods for
contribution measurement were based on enumeration over possible combination of
federated participants. Their computation costs increase drastically with the
number of participants or feature dimensions, making them inapplicable in
practical situations. In this paper an efficient method is proposed to evaluate
the contributions of federated participants. This paper focuses on the
horizontal FL framework, where client servers calculate parameter gradients
over their local data, and upload the gradients to the central server. Before
aggregating the client gradients, the central server train a data value
estimator of the gradients using reinforcement learning techniques. As shown by
experimental results, the proposed method consistently outperforms the
conventional leave-one-out method in terms of valuation authenticity as well as
time complexity.
