  Client contribution evaluation, also known as data valuation, is a crucial
approach in federated learning(FL) for client selection and incentive
allocation. However, due to restrictions of accessibility of raw data, only
limited information such as local weights and local data size of each client is
open for quantifying the client contribution. Using data size from available
information, we introduce an empirical evaluation method called Federated
Client Contribution Evaluation through Accuracy Approximation(FedCCEA). This
method builds the Accuracy Approximation Model(AAM), which estimates a
simulated test accuracy using inputs of sampled data size and extracts the
clients' data quality and data size to measure client contribution. FedCCEA
strengthens some advantages: (1) enablement of data size selection to the
clients, (2) feasible evaluation time regardless of the number of clients, and
(3) precise estimation in non-IID settings. We demonstrate the superiority of
FedCCEA compared to previous methods through several experiments: client
contribution distribution, client removal, and robustness test to partial
participation.
