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The success of Federated Learning (FL) depends on the quantity and quality ofthe 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 potentialselection bias towards highly reputable DOs. Such a bias can result in lowerreputation DOs being prematurely excluded from future FL training rounds,thereby reducing the diversity of training data and the generalizability of theresulting models. To address these challenges, we propose the GradualParticipant Selection scheme for Auction-based Federated Learning (GPS-AFL).Unlike existing AFL incentive mechanisms which generally assume that all DOsrequired for an FL task must be selected in one go, GPS-AFL gradually selectsthe required DOs over multiple rounds of training as more information isrevealed through repeated interactions. It is designed to strike a balancebetween cost saving and performance enhancement, while mitigating the drawbacksof selection bias in reputation-based FL. Extensive experiments based onreal-world datasets demonstrate the significant advantages of GPS-AFL, whichreduces costs by 33.65% and improved total utility by 2.91%, on averagecompared to the best-performing state-of-the-art approach.

Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning