Federated Learning Across Hospitals: Communication Cost, Heterogeneity and What Never Leaves the Site

Authors

  • Rasa Stankevicius Centre for Responsible AI, Nemunas Delta Institute of Applied Artificial Intelligence, Silute, Lithuania Author
  • Jarmila Belanova Department of Clinical Informatics, Zahorie Institute of Applied Artificial Intelligence, Malacky, Slovakia Author
  • Roby Steichen Centre for Machine Learning Research, Sure Valley Centre for Machine Intelligence, Ettelbruck, Luxembourg Author

DOI:

https://doi.org/10.5281/zenodo.22657213

Keywords:

Federated Learning Across Hospitals; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Federated Learning Across Hospitals is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for federated learning across
hospitals, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance

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Published

2022-03-07

Issue

Vol. 1 No. 1 (2022): Vol 1 Issue 1 2022

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Articles

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