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https://hdl.handle.net/2142/120432
Description
Title
Federated domain adaptation for healthcare
Author(s)
Jiang, Enyi
Issue Date
2023-05-02
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Federated Learning, Domain Adaptation, ML for Healthcare
Abstract
Federated domain adaptation (FDA) describes the setting where a set of source clients seek to optimize the performance of a target client. To be effective, FDA must address some of the distributional challenges of Federated learning (FL). For instance, FL systems exhibit distribution shifts across clients. Further, labeled data are not always available among the clients. To this end, we propose and compare novel approaches for FDA, combining the few labeled target samples with the source data when auxiliary labels are available to the clients. The in-distribution auxiliary information is included during local training to boost out-of-domain accuracy. Also, during fine-tuning, we devise a simple yet efficient gradient projection method (FedGP) to detect the valuable components from each source client model by comparing them with the target direction. The extensive experiments on healthcare datasets show that our proposed framework outperforms the state-of-the-art unsupervised FDA methods with limited additional time and space complexity.
Additionally, we find that common techniques such as FedAvg and fine-tuning fail with a large domain shift. To better investigate the effectiveness of FedGP under various extents of domain shift, we perform extensive semi-synthetic and real-world experiments on general-purposed datasets compared with several baselines. Our results indicate a bias-variance trade-off between source and target domains when combining source and target gradients. FedGP maintains a better trade-off between source gradients' bias (the domain shift between source and target domains) and the target gradient's variance from limited labeled data. Our experiments illustrate the effectiveness of the proposed method in practice.
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