CREAT: Blockchain-Assisted Compression Algorithm of Federated Learning for Content Caching in Edge Computing

【Author】 Cui, Laizhong; Su, Xiaoxin; Ming, Zhongxing; Chen, Ziteng; Yang, Shu; Zhou, Yipeng; Xiao, Wei

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes' data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.

【Keywords】Edge computing; Internet of Things; Data models; Cloud computing; Load modeling; Task analysis; Blockchain; edge computing; federated learning (FL); gradients compression

【发表时间】2022 AUG 15

【收录时间】2022-08-28

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-边缘计算