Blockchain-Assisted Federated Hybrid Deep Learning for Secure and Privacy-Preserving Skin Cancer Diagnosis

Skin cancer is one of the most prevalent and fastest-growing forms of cancer globally, and late diagnosis is still a significant issue. Despite the success of deep learning for dermoscopic image analysis, centralized training is plagued by the limitations of data-privacy regulations (HIPAA, GDPR), non-uniform data distributions between rare lesion types, and problems of adversarial and poisoning attacks in common computing environments. The complementary solutions to these challenges are federated learning (FL), hybrid deep learning (HDL) and blockchain, respectively, as they preserve privacy, enhance the robustness of features by architectural fusion and offer tamper-resistant, audit-able aggregation. This paper summarizes the current status of the development of each paradigm, shows that, on their own, there are still significant knowledge gaps, and outlines an integrated approach and a pre-registered experimental protocol for a secure, scalable, multi-institutional skin cancer diagnosis system that brings all three paradigms together.