  Blockchain-based federated learning has gained significant interest over the
last few years with the increasing concern for data privacy, advances in
machine learning, and blockchain innovation. However, gaps in security and
scalability hinder the development of real-world applications. In this study,
we propose ScaleSFL, which is a scalable blockchain-based sharding solution for
federated learning. ScaleSFL supports interoperability by separating the
off-chain federated learning component in order to verify model updates instead
of controlling the entire federated learning flow. We implemented ScaleSFL as a
proof-of-concept prototype system using Hyperledger Fabric to demonstrate the
feasibility of the solution. We present a performance evaluation of results
collected through Hyperledger Caliper benchmarking tools conducted on model
creation. Our evaluation results show that sharding can improve validation
performance linearly while remaining efficient and secure.
