Wednesday, March 31, 2021 - 4:00pm to 4:30pm
Event Calendar Category
LIDS & Stats Tea
Andreas Alexander Haupt
LIDS & IDSS
Zoom meeting id
990 8291 2931
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Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While FL is successfully applied in environments where a central orchestrator has the primary interest in the shared model’s quality, incentive problems complicate FL in settings with self-interested, heterogeneous clients. In this talk, we model FL incentive design as a digital goods double auction with a submodular production function. Inspired by existing prior-free auction designs, we present a prior-free mechanism that constant-factor approximates optimal revenue. We introduce how “model leakage”, a measure of model sharing between clients beyond the control of the designer, impacts mechanism performance. To facilitate further study, we present code that allows testing incentive designs for federated learning using clients' training on data splits from the MNIST, Fashion-MNIST, and CIFAR-10 datasets. A potential broader impact of this research might be that, for standard learning tasks, incentivized FL might allow for privacy-preserving, sustainable, and scalable data transactions.
Joint work with V. Mugunthan.
Link to paper: https://arxiv.org/abs/2103.14375
Andreas Haupt is a graduate student at the Institute for Data, Systems, and Society (IDSS), where he works with Munther Dahleh and Alessandro Bonatti. Prior to joining IDSS, Andreas received master’s degrees in Mathematics and Economics and bachelor’s degrees in Computer Science and Mathematics from the universities of Bonn and Frankfurt. In 2019, he was an MIT Presidential Fellow. Andreas conducts research on the role of communication and complexity in market design and is interested in the designs of markets for information. He has interned in digital policy for the German federal parliament and the European Union’s competition authority in the unit specialized on data markets.