Enhancing quality of service through federated learning in edge-cloud architecture

The traditional cloud computing paradigm faces challenges with the increasing number of Artificial Intelligence of Things (AIoT) devices generated at the network edge. Edge computing provides a novel approach to overcoming the limitations of cloud computing by delivering lower service latency and higher quality of service (QoS) to AIoT devices. However, edge computing encounters constraints due to scarce resources on edge servers and the long distance between AIoT devices and remote cloud servers. To ensure high QoS for AIoT devices, a balance is required between limited computing resources on the network edge and high latency caused by the geographic distance on the cloud side. In this paper, we propose an edge-cloud architecture that achieves optimal QoS for AIoT devices. We employ a federated learning-based architecture to train AIoT devices’ data locally, thereby ensuring privacy. We evaluate the effectiveness and efficiency of our proposed approach by comparing it with the centralized approach from the state-of-the-art using two widely used datasets. The experimental results demonstrate that our architecture achieves higher effectiveness and efficiency in improving AIoT devices’ QoS. Overall, our proposed edge-cloud architecture overcomes the limitations of traditional cloud computing, enhances user privacy, and delivers high QoS to AIoT devices.