MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services

The fast growth of telemedicine has made it clear that we need safe, cooperative, and privacy-protecting ways to handle sensitive medical information. Traditional centralised predictive model training methods have problems including data leaks, ownership disputes, and tight rules that make it hard for institutions to work together. To solve these problems, this paper suggests MedLedgerFL, a hybrid platform that combines blockchain with federated learning (FL) to make remote healthcare analytics safe and reliable. The blockchain layer makes sure that the model updates can be audited, can’t be changed, and can only be accessed by authorised healthcare institutions. The federated learning layer lets healthcare institutions work together to train the model without sharing patient data, which makes sure that they follow data protection laws like GDPR. Tests show that MedLedgerFL is better than other methods at making accurate predictions, communicating quickly, and keeping information private. Combining blockchain consensus with federated model aggregation makes things more open, lowers the danger of data exposure, and makes models more reliable. Future endeavours will concentrate on enhancing scalability among institutions, integrating sophisticated privacy-preserving techniques like differential privacy and homomorphic encryption, and assessing the framework’s efficacy in extensive, practical telemedicine applications.