  Contribution evaluation in federated learning (FL) has become a pivotal
research area due to its applicability across various domains, such as
detecting low-quality datasets, enhancing model robustness, and designing
incentive mechanisms. Existing contribution evaluation methods, which primarily
rely on data volume, model similarity, and auxiliary test datasets, have shown
success in diverse scenarios. However, their effectiveness often diminishes due
to the heterogeneity of data distributions, presenting a significant challenge
to their applicability. In response, this paper explores contribution
evaluation in FL from an entirely new perspective of representation. In this
work, we propose a new method for the contribution evaluation of heterogeneous
participants in federated learning (FLCE), which introduces a novel indicator
\emph{class contribution momentum} to conduct refined contribution evaluation.
Our core idea is the construction and application of the class contribution
momentum indicator from individual, relative, and holistic perspectives,
thereby achieving an effective and efficient contribution evaluation of
heterogeneous participants without relying on an auxiliary test dataset.
Extensive experimental results demonstrate the superiority of our method in
terms of fidelity, effectiveness, efficiency, and heterogeneity across various
scenarios.
