<p>The creation of effective healthcare artificial intelligence (AI) systems demands access to large and diverse datasets dispersed in various healthcare institutions. Nevertheless, strict privacy policies in the United States such as HIPAA, in Europe such as GDPR, and other laws in other countries pose a tremendous hindrance to centralized aggregation of data. Although Federated Learning (FL) allows all involved parties to jointly train models without centrally storing sensitive patient information, gradient updates exchanged in the training process may reveal much valuable personal information via more advanced reconstruction attacks. The purpose of the study is to create and fully test a privacy-guaranteed federated learning model that facilitates and federates diagnostic AI training among multiple healthcare facilities and formally guarantees some differential privacy and retains clinic-quality model performance. According to the research, we introduce PP-FL-DP (Privacy-Preserving Federated Learning with Differential Privacy), a unified system introducing three innovations: (1) Hierarchical Privacy-Preserving Architecture (HPPA) with defense-in-depth, i.e., local differential privacy, secure aggregation, and Byzantine-robust features; (2) Sensitivity-Aware Gradient Perturbation (SAGP) with layer-wise adaptive clipping, based on empirical gradient sensitivity analysis; and (3) Three different healthcare datasets, including breast cancer mammography (<i>N</i> = 12,500 images, 5 federated clients), diabetic retinopathy fundus imaging (<i>N</i> = 35,000 images, 8 clients), and ECG arrhythmia detection (<i>N</i> = 45,000 recordings, 6 clients) were thoroughly evaluated. Hyperparameters (batch size, 16,32, 64, 128, learning rate, 0.001-0.1 and aggregation threshold, τ = 0.1–0.9) were fully analyzed. PP-FL-DP had a diagnostic accuracy of 96.2 ± 0.4% and a AUC-ROC of 0.978 ± 0.003 and Precision-Recall AUC of 0.962 ± 0.004 on breast cancer detection at ε = 1.5 differential privacy -only 2.1% worse than centralized non-private training. The framework showed a 91.3% resistance to gradient inversion attacks (8.7% success rate of the attacks against 21.6% of standard DP-SGD), a 66% final training loss reduction (0.048 versus 0.142) and 2.1 times accelerated convergence (95% accuracy at round 45 compared to round 70 or more). The best hyperparameters were found to be a batch size 32, learning rate 0.01 and aggregation threshold τ = 0.5. When data heterogeneity is severe (α = 0.1), PP-FL-DP showed 91.8% reflecting a 13.5% improvement. Scalability analysis showed positive characteristics: federations of <i>N</i> = 100 clients were found to achieve high accuracy of 97.8% and effective privacy of ε = 0.94 using subsampling amplification. PP-FL-DP offers the latest privacy-utility tradeoffs that can be used to deploy privacy-aware collaborative healthcare AI in practice. The framework is effective in between regulatory compliance and clinical utility requirements and provides a basis on which the development of multi-institutional medical AI may be created. There is a notable broader societal impact to this framework: for by enabling privacy-compliant AI collaboration across diverse healthcare institutions worldwide—including those in resource-limited settings—it offers a promising solution to lowering diagnostic inequities and enhancing population-level health outcomes, while preserving patient privacy.</p>