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：联邦学习的成功依赖于数据所有者的数量与质量及其参与模型训练的积极性。现有基于声誉的联邦学习参与者选择方法虽已提出，但仍面临冷启动问题和潜在的高声誉数据所有者选择偏差。这种偏差可能导致低声誉数据所有者过早被排除在后续联邦学习训练轮次之外，从而降低训练数据的多样性及最终模型的泛化能力。为应对这些挑战，我们提出面向拍卖的联邦学习渐进式参与者选择方案（GPS-AFL）。不同于现有AFL激励机制通常假设联邦学习任务所需的所有数据所有者必须一次性完成选择，GPS-AFL通过反复交互获取更多信息，在多个训练轮次中逐步选择所需的数据所有者。该方案旨在平衡成本节约与性能提升，同时减轻声誉驱动型联邦学习中选择偏差的负面影响。基于真实数据集的广泛实验表明，GPS-AFL具有显著优势，与现有最佳方法相比，平均降低33.65%的成本并提升2.91%的总效用。