Joint Optimization Algorithm of Training Delay and Energy Efficiency for Wireless Large-Scale Distributed Machine Learning Combined With Blockchain for 6G Networks

【Author】 Zhang, Xiuxian; Zhu, Xiaorong

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】In 6G, the communication cost of large-scale distributed machine learning (DML) will be much higher than the computing cost, which will become a bottleneck restricting the development of DML. To solve this problem, a wireless large-scale DML architecture combined with blockchain (WLDMLB) for 6G networks is proposed, where the distributed nodes involved in DML are divided into shards and a layered adaptive cascaded architecture is used in each shard to reduce the communication overhead. To reduce the system energy, improve training efficiency and achieve on-demand networking, a joint optimization model of the number of shards, network topology, and allocation of computing resources is established to ensure that the model can run efficiently on different devices. Then, a closed-form expression of one-round training delay and energy is derived. The optimal number of shards, the optimal network topology and the optimal computing resource allocation are further analysed. In addition, a main-shards blockchain architecture with the directed acyclic graph (DAG) and practical Byzantine fault tolerance (PBFT) consensus is proposed to ensure the trusted sharing of model and ensure system scalability. Simulation results show that the algorithm can greatly reduce the communication overhead, one round-training delay and energy of DML.

【Keywords】6G; blockchain; directed acyclic graph (DAG); distributed machine learning (DML); practical Byzantine fault tolerance (PBFT); wireless largescale DML architecture combined with blockchain (WLDMLB); 6G; blockchain; directed acyclic graph (DAG); distributed machine learning (DML); practical Byzantine fault tolerance (PBFT); wireless largescale DML architecture combined with blockchain (WLDMLB)

【发表时间】2024 OCT 1

【收录时间】2024-10-15

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-去中心化机器学习