EffiAuL: Influence-guided approximate unlearning for privacy-preserving medical image analysis
Mohammad Zavid Parvez, Rafiqul Islam, Lihong Zheng, Md Zahidul IslamObjectives
Machine unlearning is increasingly relevant to privacy-aware medical artificial intelligence when trained models must respond to subsequent data-removal requests. This study proposes EffiAuL, an influence-guided approximate-unlearning framework designed to balance forgetting behaviour, predictive-utility preservation, and computational cost.
Methods
EffiAuL combines sample-level gradient-norm influence estimation, adaptive strategy selection, selective refinement of transfer-learned parameters, retained-data divergence control, and a validation-derived utility criterion with fallback to full retraining. The primary binary chest X-ray dataset comprised 2,330 Normal and COVID-19 images, partitioned using a fixed stratified 70/15/15 train-validation-test split and evaluated across five independent seeds. EffiAuL was compared with full retraining, SCRUB, SISA, IF-Diag, and a no-deletion FineTune-Control using predictive, forgetting-specific, membership-inference, distributional, computational, and stability measures. Additional validation used a four-class chest X-ray dataset containing 4,852 images.
Results
EffiAuL achieved a weighted F1-score of 0.9966 ± 0.0013 (95% CI: 0.9950–0.9982), compared with 0.9960 ± 0.0016 (95% CI: 0.9941–0.9979) for full retraining. The paired difference was not statistically significant (
Conclusion
EffiAuL preserves predictive utility close to full retraining while producing measurable forgetting-related behavioural changes. The findings support multidimensional evaluation of approximate unlearning rather than reliance on predictive performance or a single forgetting metric. EffiAuL provides empirical evidence of approximate unlearning and should not be interpreted as formally certified deletion or evidence of regulatory compliance.