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Federated Learning over Harmonized Data Silos

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Artificial Intelligence for Personalized Medicine (W3PHAI 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1106))

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Abstract

Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent across the different silos. However, silos often have different schemata, data formats, data values, and access patterns. The field of data integration has developed many methods to address these challenges, including techniques for data exchange and query rewriting using declarative schema map**s, and entity linkage. We propose an architectural vision for an end-to-end Federated Learning and Integration system, incorporating the critical steps of data harmonization and data imputation, to spur further research on the intersection of data management information systems and machine learning.

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Acknowledgements

This research was supported in part by the Defense Advanced Research Projects Agency (DARPA) under contract HR00112090104, and in part by the National Institutes of Health (NIH) under grant R01DA053028.

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Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, Los Angeles, CA, 90007, USA

    Dimitris Stripelis & José Luis Ambite

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  1. Dimitris Stripelis
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  2. José Luis Ambite
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Correspondence to Dimitris Stripelis .

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  1. Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center–Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Memphis, TN, USA

    Arash Shaban-Nejad

  2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  3. Altos Labs - Bay Area Institute of Science, Redwood City, CA, USA

    Simone Bianco

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Stripelis, D., Ambite, J.L. (2023). Federated Learning over Harmonized Data Silos. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Artificial Intelligence for Personalized Medicine. W3PHAI 2023. Studies in Computational Intelligence, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-031-36938-4_3

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