Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI

Despite the advancements in federated learning (FL) for privacy-preserving AI, its implementation in medical diagnostics faces persistent challenges. Some of these are intolerable computational loads, communication delays, and inefficiencies in dynamic healthcare environments. Since X-ray models are likely to be imbalanced/heterogeneous, and be prone to adversarial attacks, the likelihood of slow convergence, high complexity, and the negative effects of these with FL models are high. As is highly likely, this will result in performance loss under constraints of such level —most likely with noisy or imbalanced data. Nevertheless, to have a stable FL platform for the actual healthcare utilization, it is essential to overcome these challenges. These challenge are responded to by proposing a new integration of ResNet-50 and privacy preserving techniques for federated medical diagnosis. The latter is a data privacy and security, which is achieved with stochastic noise and homomorphic encryption on top of the superior classification performance of ResNet-50 in processing high dimensional X-ray images. Through effective imbalanced datasets reduction and reduced computational complexity, our method reduces convergence during training with adaptive learning rates and advanced data balancing methods. The model is applied on Python with the use of the TensorFlow and PyTorch libraries, integrating the real-time data using improved communication formula. It is also compared with other approaches and achieve a considerable improvement in accuracy of 99.6%, Precision is 98.8%, Recall is 99.2%, and F1 Score is 99% compared to the 5% improvement of conventional FL models. The model is equally stable and scalable in the dynamic case of a healthcare setting, with guarantees on effective diagnostics and data privacy. The system is a new standard for privacy friendly AI in healthcare diagnosis and gives an effective and scalable solution in X-ray classification task. Now that it’s an open door for future research, there’s further expansion of more complex privacy mechanisms and integration with other AI systems.