ABSTRACT Healthcare data holds immense potential to improve diagnostics and treatment, but centralized collection risks patient privacy and suffers from limited labeled samples. Existing methods fall short in securely leveraging distributed data while reducing annotation costs. To address this, we propose FTAL ‐ QNC —a novel Federated Transfer Active Learning framework with Quantized Neural Cryptography—that enables privacy‐preserving, communication‐efficient, and label‐efficient collaborative model training. FTAL ‐ QNC integrates four core components in a unified architecture: (1) Federated learning for decentralized model training without data sharing, (2) Transfer learning to handle data heterogeneity across hospitals, (3) Active learning to minimize manual labeling by prioritizing informative samples, and (4) Quantized neural cryptography to ensure secure, low‐overhead exchange of encrypted model updates. Empirical evaluations on real‐world datasets, including Lung CT and ChestX ‐ray14, demonstrate that FTAL ‐ QNC enhances segmentation and classification accuracy to 97.3% and 97.0% recall for Lung CT , respectively, while significantly reducing annotation effort compared to standard federated learning and other sampling methods. Our contributions include a privacy‐preserving and communication‐efficient collaborative framework, an integrated active learning mechanism for efficient data labeling, and a secure aggregation protocol via quantized neural cryptography. These results demonstrate FTAL ‐ QNC 's potential to advance safe, collaborative medical research and improve patient outcomes.
Malathy et al. (Thu,) studied this question.