OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noise
Quick summary
arXiv:2609.14734v1 Announce Type: cross Abstract: Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions cannot always be assumed. In ophthalmic imaging, differences in disease prevalence and class composition can resemble changes caused by corrupted supervision. We introduce OCT-FedSIR, a reliability-aware spectral framework for federated OCT classification under client-dependent annotation noise and heterogeneous data distributions. OCT-FedSIR combines class-balanced spectral estimation, Stage-I l
Key takeaways
- arXiv:2609.14734v1 Announce Type: cross Abstract: Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions cannot always be assumed.
- In ophthalmic imaging, differences in disease prevalence and class composition can resemble changes caused by corrupted supervision.
- We introduce OCT-FedSIR, a reliability-aware spectral framework for federated OCT classification under client-dependent annotation noise and heterogeneous data distributions.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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