FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Quick summary
arXiv:2608.16971v1 Announce Type: new Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible. We introduce FedPref: frozen public language models propose alternative JSON extractions, local annotations rank them, and sites collaboratively train compact Qwen3-8B adapters while sharing only model updates. A heterogeneous teacher poo
Key takeaways
- arXiv:2608.16971v1 Announce Type: new Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema.
- Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible.
- We introduce FedPref: frozen public language models propose alternative JSON extractions, local annotations rank them, and sites collaboratively train compact Qwen3-8B adapters while sharing only model updates.
Why it matters
“FedPref: Federated Preference Learning for Structured Radiology Report Extraction” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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