Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations

Erfan Darzidehkalani*, Mohammad Ghasemi-Rad, P M A van Ooijen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
7 Downloads (Pure)


Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging due to the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them.

Original languageEnglish
Pages (from-to)975-982
Number of pages8
JournalJournal of the american college of radiology
Issue number8
Early online date25-Apr-2022
Publication statusPublished - Aug-2022

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