An Implementation of Blockchain-based Federated Learning Framework

While deep learning technologies are rising, information security is becoming increasingly important in the modern era. Federated Learning (FL) is a method that allows different clients to collaborate on training deep learning models while ensuring data privacy. However, traditional FL faces the challenge of a single point of failure with the aggregation server if the centralized server fails. Additionally, FL often encounters malicious model attacks, leading to a reduction in global model accuracy. To address these issues, we propose Scale BlockChain Federated Learning (ScaleBCFL) using Hyperledger Fabric (HLF). By leveraging blockchain technology, we decentralize the central server in traditional FL and create multiple shards to reduce communication costs significantly. Furthermore, using a blockchain handler obviates the necessity for users to invest time in learning how to write smart contracts. Instead, users can focus solely on customizing programs related to training, aggregation, and validating models.