Auction-based Federated Learning (AFL) has attracted extensive research
interest due to its ability to motivate data owners to join FL through economic
means. Existing works assume that only one data consumer and multiple data
owners exist in an AFL marketplace (i.e., a monopoly market). Therefore, data
owners bid to join the data consumer for FL. However, this assumption is not
realistic in practical AFL marketplaces in which multiple data consumers can
compete to attract data owners to join their respective FL tasks. In this
paper, we bridge this gap by proposing a first-of-its-kind utility-maximizing
bidding strategy for data consumers in federated learning (Fed-Bidder). It
enables multiple FL data consumers to compete for data owners via AFL
effectively and efficiently by providing with utility estimation capabilities
which can accommodate diverse forms of winning functions, each reflecting
different market dynamics. Extensive experiments based on six commonly adopted
benchmark datasets show that Fed-Bidder is significantly more advantageous
compared to four state-of-the-art approaches.

许多现有的拍卖式联邦学习假设只有一个数据消费者和多个数据所有者参与，在实际的联邦学习市场中有多个数据消费者可以竞争吸引数据所有者加入任务中。本文提出了一种新型的拍卖式联邦学习数据消费者效用最大化竞标策略（Fed-Bidder），它通过提供适应不同的市场动态的中标功能来有效地和高效地使多个联邦学习数据消费者竞争数据所有者的加入。基于六个常用基准数据集的广泛实验表明，Fed-Bidder 相对于四种最先进的方法具有显著的优势。