计算机科学
效率低下
计算
联合学习
通信卫星
边缘计算
GSM演进的增强数据速率
分布式计算
卫星
人工智能
深度学习
机器学习
算法
工程类
经济
微观经济学
航空航天工程
作者
Weiwei Jiang,Hongqi Han,Yang Zhang,Jianbin Mu
标识
DOI:10.1016/j.inffus.2023.102141
摘要
Satellite-terrestrial integrated networks (STINs) have been proposed for B5G/6G mobile communication, and the increase in the computation and communication capacities of satellites opens the door for satellite edge computing in space. The requirements of privacy protection and communication efficiency lead to the inefficiency or deficiency of traditional learning approaches in STINs. This study incorporates federated learning and split learning paradigms with STINs and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. A case study of electricity theft detection based on a real-world sequential power load dataset is utilized to demonstrate the effectiveness of the proposed solution. Numerical experiments show that the proposed FedSL-LSTM model is an effective federated learning solution and a competitive solution compared with centralized models in terms of classification performance evaluation metrics.
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