The success of Federated Learning (FL) depends on the quantity and quality of the data owners (DOs) as well as their motivation to join FL model training. Reputation-based FL participant selection methods have been proposed. However, they still face the challenges of the cold start problem and potential selection bias towards highly reputable DOs. Such a bias can result in lower reputation DOs being prematurely excluded from future FL training rounds, thereby reducing the diversity of training data and the generalizability of the resulting models. To address these challenges, we propose the Gradual Participant Selection scheme for Auction-based Federated Learning (GPS-AFL). Unlike existing AFL incentive mechanisms which generally assume that all DOs required for an FL task must be selected in one go, GPS-AFL gradually selects the required DOs over multiple rounds of training as more information is revealed through repeated interactions. It is designed to strike a balance between cost saving and performance enhancement, while mitigating the drawbacks of selection bias in reputation-based FL. Extensive experiments based on real-world datasets demonstrate the significant advantages of GPS-AFL, which reduces costs by 33.65% and improved total utility by 2.91%, on average compared to the best-performing state-of-the-art approach.

翻译：联邦学习（FL）的成功依赖于数据所有者（DO）的数量与质量，以及他们参与FL模型训练的动机。基于声誉的FL参与者选择方法已被提出，但仍面临冷启动问题和可能偏向高声誉DO的选择偏见。这种偏见可能导致低声誉DO被过早排除在未来的FL训练轮次之外，从而降低训练数据的多样性和最终模型的泛化能力。为应对这些挑战，我们提出了面向拍卖式联邦学习的渐进式参与者选择方案（GPS-AFL）。与现有AFL激励机制通常假设一次性地选出FL任务所需的所有DO不同，GPS-AFL通过反复交互获取更多信息后，在多个训练轮次中渐进式地选出所需的DO。该方案旨在平衡成本节约与性能提升，同时缓解基于声誉的FL中由选择偏见带来的弊端。基于真实数据集的广泛实验表明，GPS-AFL具有显著优势，与性能最优的现有方法相比，平均成本降低33.65%，总效用提升2.91%。