Abstract
With the rapid development of artificial intelligence (AI), combining machine learning (ML) and edge computing powered by big data has become a growing trend. However, under edge computing, ML faces problems such as limited energy consumption, insufficient computing power, and data security threats. Therefore, we propose a ubiquitous intelligent federated learning privacy protection scheme(UIFLPP), which provides privacy protection for data under edge computing. First, we train part of the models in embedded devices and add matrix masks to ensure secure transmission between embedded devices and edge servers. In addition, residual model training is performed on the edge server with a differential privacy mechanism. After aggregation in the cloud, noise is added and the model is fed back to the edge server. The experimental results show that the scheme can achieve 86% accuracy on CIFAR10 on the premise of ensuring privacy and reducing the training time by 5% compared with the baseline scheme, which can well meet the needs of ML in edge scenarios.
•Propose a architecture of federated learning with cloud–edge-device collaboration under edge computing to solve the problem of insufficient computing power and privacy in embedded devices.•Design a lightweight privacy protection scheme that trains part of models in embedded devices and adds matrix masks to ensure secure transmission between embedded devices and edge servers.•The residual model training is performed on the edge server with differential privacy mechanism; after aggregation in the cloud, the noise is added and fed back to the edge server.