Transformers on Encrypted Federated Datasets Anchored by Blockchain Zero-Knowledge Proofs for Privacy-Preserving Multilingual Healthcare Diagnostics and Equity.

The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts. Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns. Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier. This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics. Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts. Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information. This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees. The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations. By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets. Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework’s capacity to balance accuracy, privacy, and fairness. Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.