121 citations · 237 across the 9 of their papers we have counts for
10 papers
From Cloud to Edge: A First Look at Public Edge Platforms
Mengwei Xu, Zhe Fu, Xiao Ma +7
Public edge platforms have drawn increasing attention from both academia and industry. In this study, we perform a first-of-its-kind measurement study on a leading public edge plat…
Hierarchical Federated Learning through LAN-WAN Orchestration
Jinliang Yuan, Mengwei Xu, Xiao Ma +3
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL pro…
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang, Qipeng Wang, Mengwei Xu +4
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is h…
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin +7
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where…
Cooperative Service Caching and Workload Scheduling in Mobile Edge Computing
Xiao Ma, Ao Zhou, Shan Zhang +1
Mobile edge computing is beneficial to reduce service response time and core network traffic by pushing cloud functionalities to network edge. Equipped with storage and computation…
Security modeling and efficient computation offloading for service workflow in mobile edge computing
Binbin Huang, Zhongjin Lia, Peng Tang +5
It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge…