Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, clients with private and potentially heterogeneous data and compute resources come together to train a common model without raw data ever leaving their locale. Instead, the participants contribute by sharing local model updates, which, naturally, differ in quality. Quantitatively evaluating the worth of these contributions is termed the Contribution Evaluation (CE) problem. We review current CE approaches from the underlying mathematical framework to efficiently calculate a fair value for each client. Furthermore, we benchmark some of the most promising state-of-the-art approaches, along with a new one we introduce, on MNIST and CIFAR-10, to showcase their differences. Designing a fair and efficient CE method, while a small part of the overall FL system design, is tantamount to the mainstream adoption of FL.

翻译：联邦学习（FL）在实体希望协作训练模型同时保持数据隐私和治理的场景中引起了越来越多的关注。在联邦学习中，拥有私有且可能异构的数据和计算资源的客户端共同训练一个通用模型，而原始数据无需离开本地。参与者通过共享本地模型更新来做出贡献，这些更新自然具有质量差异。量化评估这些贡献的价值被称为贡献评估（CE）问题。我们从基础数学框架出发，综述了现有的贡献评估方法，以高效计算每个客户端的公平价值。此外，我们还在MNIST和CIFAR-10数据集上对一些最有前景的最新方法（包括我们引入的一种新方法）进行了基准测试，以展示它们的差异。设计一种公平且高效的贡献评估方法，虽然只是整体联邦学习系统设计中的一小部分，但对于联邦学习的主流采用至关重要。