  Federated Learning (FL) creates an ecosystem for multiple agents to
collaborate on building models with data privacy consideration. The method for
contribution measurement of each agent in the FL system is critical for fair
credits allocation but few are proposed. In this paper, we develop a real-time
contribution measurement method FedCM that is simple but powerful. The method
defines the impact of each agent, comprehensively considers the current round
and the previous round to obtain the contribution rate of each agent with
attention aggregation. Moreover, FedCM updates contribution every round, which
enable it to perform in real-time. Real-time is not considered by the existing
approaches, but it is critical for FL systems to allocate computing power,
communication resources, etc. Compared to the state-of-the-art method, the
experimental results show that FedCM is more sensitive to data quantity and
data quality under the premise of real-time. Furthermore, we developed
federated learning open-source software based on FedCM. The software has been
applied to identify COVID-19 based on medical images.
