A Sharded Blockchain Architecture for Healthcare Data

The application of machine learning (ML) tech-
niques to electronic health records (EHR) is gaining more and
more attention as a method to extract valuable information that
has the potential to enhance the decision-making process within
the healthcare domain. A useful approach comes from the fed-
erated learning (FL) scenario, which facilitates the decentralised
training of machine learning models using datasets that are stored
locally, hence eliminating the necessity of data aggregation on
a central server. Federated learning also ensures data privacy
because the federated devices do not share the actual data and
store it locally. It becomes a useful tool when integrated with
blockchain technology, which provides some properties such as
immutability and traceability that are useful to enhance the
security of such applications. With the growing use of IoT
healthcare (IoHT) devices, it is becoming challenging to manage
them centrally and ensuring the healthcare data privacy. In this
work, we propose an architecture to address the scalability issue
related to the healthcare data management for federated learning
networks with a sharding-based blockchain technique. We discuss
some basic properties and report some results also coming from
the implementation in Hyperledger Fabric.