Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning

TL;DR

This paper introduces Fed-Bidder, a novel bidding strategy for multiple data consumers in auction-based federated learning, enhancing competition and utility estimation in realistic markets.

Contribution

It proposes the first utility-maximizing bidding strategy for multiple data consumers in AFL, addressing a gap in existing monopoly-based models.

Findings

Fed-Bidder outperforms four state-of-the-art approaches.

It effectively estimates utility across diverse market dynamics.

Experimental results on six benchmark datasets demonstrate significant advantages.

Abstract

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.