When Calibration Fails the Vulnerable Hospital: Federated Conformal Risk Control via Risk-Curve Shrinkage
arXiv:2606.20115v1 Announce Type: new Abstract: Conformal risk control (CRC) provides distribution-free guarantees on segmentation quality by calibrating a prediction-set threshold on held-out data. In federated deployments, the standard approach pools calibration scores across sites into a single threshold. We provide the first quantification, on real multi-institutional brain tumor data (FeTS-2022, 1,251 subjects, 20 institutions), showing that this naive pooled CRC protects the average hospital but violates coverage at 40% of individual institutions, with the worst site exceeding the target
The proliferation of federated learning in critical applications like healthcare is exposing previously unaddressed vulnerabilities in standard calibration methodologies for AI models.
This research highlights a critical failure point in AI deployment for sensitive sectors, where seemingly robust global models can disproportionately harm or misclassify vulnerable populations or institutions.
The understanding that simple pooled calibration in federated AI is inadequate and can lead to unacceptable failure rates at individual institutional levels, necessitating more sophisticated, site-specific calibration methods.
- · AI fairness researchers
- · Healthcare AI developers
- · Robust AI solution providers
- · Hospitals with poorer data quality
- · Naively deployed federated AI systems
- · Current standard federated learning practices
Demand will increase for federated learning techniques that ensure equitable performance across all participating entities, not just the aggregate.
New regulatory frameworks may emerge to mandate or incentivize calibration methodologies that account for site-specific performance in federated AI deployments, particularly in healthcare.
This could lead to a re-evaluation of trust in federated AI systems in other sensitive sectors, prompting similar studies and methodological improvements beyond healthcare.
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Read at arXiv cs.LG