Compressive-Learning-Based Federated Learning for Intelligent IoT With Cloud–Edge Collaboration
Xianwei Gao, Lingyu Hou, Bi Yu Chen, Yao Xiang, Zhufeng Suo
Abstract
Resource-constrained Intelligent Internet of Things (IoT) environments often grapple with the challenges of security and efficiency. To this end, we present a collaborative cloud-edge IoT framework based on compressive learning (CL) and federated learning (FL), called FCL. The end sensors employ compressive sampling to simultaneously accomplish data dimensionality reduction and lightweight privacy protection. Subsequently, edge devices utilize CL algorithms for data training, and the resulting models are uploaded to the cloud server for global aggregation. Experimental results have validated the effectiveness of the proposed scheme, in which the Transformer-based FCL still achieves nearly 80% accuracy when the computation and communication overheads are reduced by 66% and 99%, respectively.