Prior-Free Auctions for the Demand Side of Federated Learning

Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While largely decentralized, FL requires resources to fund a central orchestrator or to reimburse contributors of datasets to incentivize participation. Inspired by insights from prior-free auction design, we propose a mechanism, FIPFA (Federated Incentive Payments via Prior-Free Auctions), to collect monetary contributions from self-interested clients. The mechanism operates in the semi-honest trust model and works even if clients have a heterogeneous interest in receiving high-quality models, and the server does not know the clients' level of interest. We run experiments on the MNIST dataset to test clients' model quality under FIPFA and FIPFA's incentive properties.