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.

翻译：基于拍卖的联邦学习（Auction-based Federated Learning, AFL）因其通过经济手段激励数据所有者参与联邦学习的能力，已引起广泛研究兴趣。现有研究假设AFL市场中仅存在一个数据消费者和多个数据所有者（即垄断市场），因此数据所有者为参与数据消费者的联邦学习任务而竞标。然而，这一假设在现实AFL市场中并不成立——实际市场中多个数据消费者会竞相吸引数据所有者加入其各自的联邦学习任务。为弥合这一差距，本文首次提出了联邦学习中数据消费者的效用最大化竞标策略（Fed-Bidder）。该策略通过提供兼容多种获胜函数（每种函数反映不同市场动态）的效用估计能力，使多个联邦学习数据消费者能够高效且有效地通过AFL竞争数据所有者。基于六个常用基准数据集的广泛实验表明，Fed-Bidder相较于四种最先进方法具有显著优势。