Blockchain Meets Federated Learning in Healthcare: A Systematic Review With Challenges and Opportunities
Abstract
Recently, innovations in the Internet of Medical Things (IoMT), information and communication technologies, and machine learning (ML) have enabled smart healthcare. Pooling medical data into a centralized storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated learning (FL) overcomes the prior problems with a centralized aggregator server and a shared global model. However, there are two technical challenges: 1) FL members need to be motivated to contribute their time and effort and 2) the centralized FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralized fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasize three main research streams based on a systematic analysis of blockchain-empowered: 1) IoMT; 2) electronic health records (EHRs) and electronic medical records (EMRs) management; and 3) digital healthcare systems (internal consortium/secure alerting). In…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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