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.

翻译：联邦学习中的贡献评估因其在检测低质量数据集、增强模型鲁棒性及设计激励机制等多个领域的适用性，已成为关键研究方向。现有贡献评估方法主要依赖数据量、模型相似度和辅助测试数据集，已在多种场景中取得成功。然而，由于数据分布的异构性，这些方法的有效性往往降低，对其适用性构成了重大挑战。为此，本文从表征的全新视角探索联邦学习中的贡献评估。本研究提出了一种用于联邦学习中异构参与者贡献评估的新方法（FLCE），该方法引入了一种新颖指标——\emph{类别贡献动量}，以执行精细化的贡献评估。我们的核心思想是从个体、相对和整体三个视角构建并应用类别贡献动量指标，从而在不依赖辅助测试数据集的情况下，实现对异构参与者有效且高效的贡献评估。大量实验结果表明，我们的方法在多种场景下的保真度、有效性、效率及异构性方面均表现出优越性。