Federated Learning in Mobile Edge Networks: A Comprehensive Survey Base Station Association: 在密集的网络中,优化Base Station Association以限制用户所面临的干扰是很重要的。 存在中断(BIPs)事件可能是信息传输延迟的结果,当用户的身体运动阻碍了无线链路时,就会造成这种延迟。 现有的方法存在的问题: traditional clo...
Federated Learning in Mobile Edge Networks: A Comprehensive Survey Authors Wei Yang Bryan Lim、Nguyen Cong Luong、Dinh Thai Hoang、Yutao Jiao、Ying-Chang
Edge Caching and Computation Offloading: 考虑到边缘服务器的计算和存储容量限制,终端设备的一些计算密集型任务必须卸载到远程云服务器进行计算。 此外,通常请求的文件或服务应放置在边缘服务器上以便更快地检索,即用户在想要访问这些文件或服务时不必与远程云通信。 因此,可以使用 FL 协同学习和优化最佳缓存和计算卸载...
thehighvariabilityinthecontentrequestsmakespredictiondemandnottrivialand;typically;themajorityoftheclassicalpredictionapproachesrequirethegatheringofpersonalusers'informationatacentralunit;givingrisetomanyusers'privacyissues.Inthiscontext;federatedlearninggainedattentionasasolutiontoperformlearningproceduresfromdatadisseminated...
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning proce...
The Cloud-Edge collaborative split learning in the U-shape configuration is a privacy-preserving variation, as shown in Fig. 1. The Edge-side includes various mobile devices, such as tablets, mobile phones, and laptops) with limited computational resources but various data sources. Edge devices ...
Conference paper Poster: Migrating running applications across mobile edge clouds
论文笔记——Federated learning framework for mobile edge computing networks 本论文着重研究的是联邦学习应用于需求预测类问题。 一般来说,FL存在的一些问题: 非独立同分布数据。客户训练数据集各不相同,给定的本地训练数据集不代表人口分布。 不平衡数据集。每个客户的本地训练数据量不同。这意味着不同客户对训练...
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao, "Federated Learning in Mobile Edge Networks: A Comprehensive Survey," Sep. 2019. Available:https://arxiv.org/abs/1909.11875 ...
Security, privacy and trust for smart mobile-internet of things (M-IoT): A survey IEEE Access (2020) LimW.Y.B. et al. Federated learning in mobile edge networks: A comprehensive survey IEEE Commun. Surv. Tutor. (2020) YangQiang et al. Federated machine learning: Concept and applications...