This review examines how Federated Learning (FL) and Blockchain (BC) can work together to support privacy-preserving, auditable and scalable AI in healthcare, with a focus on Ayurveda. Using a structured database search and clear inclusion and exclusion criteria, We reviewed 87 papers mainly from 2020 to 2025, plus few of foundational works published earlier on Federated Learning in healthcare, Blockchain—Federated Learning frameworks, Deep Learning (DL) integrated with Federated Learning and Blockchain, and AI-based work in Ayurveda, Explainability in Healthcare AI guided by five research questions. The findings show that FL enables multi-centre model training without sharing raw data, while BC adds tamper-evident logging, fine-grained access control and incentive mechanisms. Integrated strong privacy methods and consensus choices create trade-offs in latency, energy use, scalability and system complexity, which must be balanced against the needs of each healthcare setting. The Ayurveda-focused analysis shows that existing AI systems are mostly small-scale, centralised and weak in provenance and privacy, so issues of data scarcity, robustness and trustworthy deployment remain unresolved. As a future direction, the review proposed a Proof of Authority (PoA) style, reputation and gradient aware with Deep Federated Learning (DFL) integrated Blockchain framework for Prakriti and Dosha-informed personalised Ayurvedic care, offering a focused roadmap for privacy preserving compliance adhering yet accurate deployment.
Karmode et al. (Fri,) studied this question.