Design and Optimization of Blockchain-Based Federated Learning for Intelligent Endogenous 6G Networks

Currently, federated learning (FL) is attracting increasing attention in the research of intelligent endogenous 6G networks. However, the increasing number of users and growing model sizes of FL cause considerable communication overhead. To reduce this communication overhead and improve the efficiency of FL, we design a hierarchical blockchain-based FL architecture, which divides a network into edge and center layers and further divides the edge layer into different shards. In addition, due to the massive scales of 6G networks, a main-shard blockchain architecture with IOTA and the practical byzantine fault tolerance (PBFT) consensus algorithm are used to ensure the safe sharing of the model and improve the transactions per second (TPS) of the system. Moreover, comprehensively considering the node computing resources, number of training samples, blockchain consensus delay, network communication delay, and FL training delay, we also establish a learning efficiency optimization model for FL and deduce the training round delay for the main and shard chains. Furthermore, the influences of different numbers of shards on the learning efficiency levels of different FL models are analyzed. The simulation results show that the proposed algorithm greatly improves the learning efficiency, increases the system TPS and reduces the communication overhead by more than 30%.