HyFed: A Hybrid Blockchain Empowered Federated Learning Privacy Fair Framework

This study presents the meticulous construction of a robust experimental system framework based on a hybrid blockchain network, designed to meet the experimental needs of federated learning research. The framework leverages TensorFlow Federated (TFF) to facilitate the optimization and substitution of federated learning aggregation algorithms and the development of reputation and contribution systems. The hybrid blockchain network architecture within this framework combines the advantages of public and private blockchains, capable of processing public transactions and managing private data. An innovative data encryption and access control mechanism has been implemented, ensuring data privacy and security. Performance optimizations, including the acceleration of block production speed and database query optimization, have been carried out to enhance system efficiency. This article provides a comprehensive deployment of the framework and an analysis of its components, offering a foundation for further research. With the evolution of federated learning and blockchain technology, the proposed experimental system framework is expected to have broader application prospects and research value.