Yuhong Jiang

TL;DR: EdgeGO, a mobile resource-sharing framework that employs mobile edge servers to provide a cost-effective deployment of 6G edge computing, which enables edge resource sharing for massive IoT devices is proposed.

TL;DR: RaFed, a resource allocation scheme for FL that proposes a heuristic to select appropriate devices to achieve a good trade-off between the interference and convergence time, and shows that compared to the state of the art works, Rafed significantly reduces the latency.

Abstract: Mobile crowd sensing (MCS), as a novel paradigm that coordinates a crowd of distributed devices to complete a whole sensing task, has attracted tremendous attention. While providing an effective and practical approach for sensing in largescale mobile scenes, the existing works on MCS suffer from a risk of privacy leakage because user data needs to be gathered in the cloud for processing and analysis. Federated Learning (FL) is a promising alternative as it can leverage mobile devices to accomplish a large learning task without centrally collecting the user data. However, incorporating FL into MCS is a non-trivial task due to the following reasons: 1) the data quality of mobile devices is often unreliable, especially in the context of crowd sensing; 2) the existing incentive mechanism in MCS may not work due to the lack of access to the user data. To address the problem, we propose a privacy-preserving mobile crowd sensing system based on Federated Learning with unreliable user data (called F-Sense). We analyze the key issues of sensing tasks, and further design an incentive mechanism to reward and motivate participants. Moreover, we explore to construct a federated quality model of user data in order to improve the data quality and obtain better training results for sensing tasks. Extensive simulation results show that F-Sense achieves privacy-preserving crowd sensing and the developed incentive mechanism can improve the task efficiency by encouraging local training at mobile devices.

TL;DR: In this article, a virtual network function backup and disposition method is proposed to deal with a problem of no consideration for diversity of the VNF and a bottom edge server, realizing high availability and high efficiency VNF disposition, and improving resource utilization rate and availability of the network.

TL;DR: This paper formulate the Service Function Chaining (SFC) embedding problem (which is NP-hard) and propose an intuitive solution for service function chaining for several typical cases and proposes a general heuristic algorithm to solve the SFC embedding problems.