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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.
