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FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Not provided in the abstract
federated learningmedical AIdata privacynatural language processing
2608.16971
Builder Relevance
2h ago80%
Abstract
FedPref enables institutions with unequal, unpooled data to collaboratively train models for structured radiology report extraction without sharing sensitive data.
Reality Card
Core Claim
FedPref improves client-mean F1 by 2.49 points and worst-site F1 by 9.10 points compared to isolated training, particularly benefiting sites with less data.
Method / Result
FedPref achieves a client-mean F1 of 68.68 on a locked, manually validated gold test set.
Limitations
The paper does not specify the authors, which may limit reproducibility and transparency.
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