Blockchain-Integrated Network for Secure Data Sharing in Collaborative NLP Research Applications

Transformers using blockchain technology allow institutions to share data, and update models, for collaborative research in natural language processing (NLP) while preserving anonymity. Our method implements verified, immutable data transfer formats while preserving the ability to train models. Most existing solutions rely either on centralized servers or old encryption methods which open up sensitive data to breaches and limit their scalability. Traditional attention systems do not consider trust or data provenance, thus compromising the reliability of collaborative model updates. Quantum-Resilient Federated Attention Blockchain (QRFAB) is the architecture proposed in this work to address these issues. QRFAB smoothly integrates transformer node federated learning with blockchain-based update tracking using quantum-resistant cryptographic hashes. A federated attention module dynamically weights each node’s contributions based on blockchain verification trust ratings, providing privacy, security, and reliable cross-institutional collaboration. Hospitals may train language models using private patient records without disclosing data in collaborative clinical NLP model construction. QRFAB ensures regulatory compliance by boosting institution generalizability. Experimental results show that QRFAB maintains model performance comparable to centralized training while providing robust data security, verifiable updates, and scalability. These findings highlight its potential to enable secure, privacy-preserving, and trustworthy collaborative NLP research in sensitive domains.