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

purposeFederated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices-selecting the "best" global model based only on validation data from participating hospitals-can lead to suboptimal deployment choices. We identify this critical failure mode as performance leakage, where the selected model overfits internal federation data and fails to generalize to unseen institutions, thereby undermining the core goal of FL: robust real-world generalization.

methodWe propose GEN-Guard, a practical post-hoc framework to detect and correct generalization failures in federated surgical AI. It integrates Generalization Detection via Client-Blocked Evaluation (CBE), which validates performance on isolated client distributions to prevent performance leakage, and Generalization Correction through Disagreement-Aware Distillation (DAD), which learns adaptive feature-level corrections for cross-institutional robustness. Both components operate after standard FL convergence while providing robust support for zero-shot adaptation to unseen clinical environments.

resultsWe first quantify the severity of performance leakage, observing Model Selection Failures (MSFs) exceeding 80% under standard evaluation. GEN-Guard is evaluated on two multi-center clinical challenges: surgical phase recognition in laparoscopic cholecystectomy and polyp segmentation in colonoscopy. Across both datasets, GEN-Guard consistently corrects these failures, improving in-federation F1 scores by up to 2 points, unseen-institution performance by up to 3 points, and worst-case institutional performance by 3-9 points.

conclusionsPerformance leakage represents a systematic and previously under-recognized risk in federated surgical AI. GEN-Guard provides a practical, privacy-preserving solution for detecting and correcting such failures without modifying the core federated training procedures. By improving cross-institutional robustness and zero-shot generalization, it strengthens the reliability of FL for real-world surgical deployment.