With the rapid development of artificial intelligence and increasing data generated by end devices, the traditional cloud-centric data processing is gradually replaced by intelligent edge computing to achieve faster and nearer service via breaking the limit of network bandwidth and communication delay. However, training machine learning (ML) models on end devices is severely resource-constrained; besides, the privacy protection and continuous improvement of ML models are challenging. To address these problems, we propose an ML model training architecture to achieve intelligent edge computing in a novel cloud-edge-device cooperative manner, which is consisted of two phases: (1) the cooperative federated pre-training phase between the cloud and edge server is inspired by federated learning, coming with an incentive mechanism for fair reward allocation according to the contribution of edge servers for pre-training the model; (2) the privacy-preserving model segmentation training phase between the edge server and device leverages homomorphic encryption to realize model improvement and protection on end devices while transferring a large amount of computation to edge servers. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and feasibility of our proposed framework.

随着人工智能的快速发展和终端设备生成的数据的增加，传统的以云为中心的数据处理逐渐被智能边缘计算所取代，从而突破了网络带宽和通信延迟的限制，从而实现了更快，更近的服务。但是，最终设备上的训练机器学习（ML）模型受到严重的资源限制；此外，机器学习模型的隐私保护和持续改进也具有挑战性。为了解决这些问题，我们提出了一种ML模型训练架构，以一种新颖的云边缘设备协作方式实现智能边缘计算，它包括两个阶段：（1）协作联合预训练云和边缘服务器之间的阶段受到联合学习的启发，并根据边缘服务器对模型的预训练的贡献提供了一种公平奖励分配的激励机制。（2）边缘服务器和设备之间的隐私保护模型分段训练阶段利用同态加密在终端设备上实现模型改进和保护，同时将大量计算转移到边缘服务器。基于综合和真实数据的大量仿真证明了我们提出的框架的有效性和可行性。