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GEN-Guard: correcting generalization failures for deployable federated surgical AI.

Julia Alekseenko^1,2, Pietro Mascagni^3,4,

¹CNRS, INSERM, ICube, UMR7357, University of Strasbourg, Strasbourg, France. alekseenko@unistra.fr.

International Journal of Computer Assisted Radiology and Surgery

|June 14, 2026

Summary

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This summary is machine-generated.

Federated Learning (FL) in surgical AI can fail due to performance leakage. Our GEN-Guard framework detects and corrects these generalization failures, improving model reliability for real-world surgical deployment.

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Federated Learning (FL) enables collaborative AI model training on sensitive surgical data without direct sharing.
Standard FL evaluation can lead to suboptimal deployment due to performance leakage, where models overfit internal data and fail to generalize.
This undermines the goal of robust real-world generalization in surgical AI.

Purpose of the Study:

To identify and address the critical failure mode of performance leakage in federated surgical AI.
To propose a practical framework, GEN-Guard, for detecting and correcting generalization failures in FL models.
To enhance the cross-institutional robustness and zero-shot adaptation capabilities of surgical AI.

Main Methods:

Introduction of GEN-Guard, a post-hoc framework for federated surgical AI.

Keywords:

Federated generalization Federated learning Federated surgical AI

Related Experiment Videos

Integration of Generalization Detection via Client-Blocked Evaluation (CBE) to prevent performance leakage.

Implementation of Generalization Correction through Disagreement-Aware Distillation (DAD) for adaptive feature-level corrections.

Main Results:

Quantification of performance leakage, revealing Model Selection Failures (MSFs) exceeding 80% under standard evaluation.
GEN-Guard demonstrated effectiveness in surgical phase recognition and polyp segmentation tasks.
Consistent improvements observed: up to 2 points in-federation F1 scores, up to 3 points in unseen-institution performance, and 3-9 points in worst-case institutional performance.

Conclusions:

Performance leakage is a significant, under-recognized risk in federated surgical AI.
GEN-Guard offers a privacy-preserving, practical solution to detect and correct generalization failures post-training.
The framework enhances FL reliability for real-world surgical applications by improving robustness and zero-shot generalization.