activity
20162021
most citedIntelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling

9 citations · 35 across the 10 of their papers we have counts for

collaborators

14 papers

cs.LG2021

Convergence Analysis and System Design for Federated Learning over Wireless Networks

Shuo Wan, Jiaxun Lu, Pingyi Fan +3

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. Ho…

cs.NI20209 cited

Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling

Shuo Wan, Jiaxun Lu, Pingyi Fan +1

Mobile edge computing (MEC) has been considered as a promising technique for internet of things (IoT). By deploying edge servers at the proximity of devices, it is expected to prov…

cs.NI20193 cited

Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System

Shuo Wan, Jiaxun Lu, Pingyi Fan +1

Heavy data load and wide cover range have always been crucial problems for online data processing in internet of things (IoT). Recently, mobile-edge computing (MEC) and unmanned ae…

cs.NI2019

Towards Big data processing in IoT: network management for online edge data processing

Shuo Wan, Jiaxun Lu, Pingyi Fan +1

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly pr…

stat.ML2018

Model change detection with application to machine learning

Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model…

eess.SP2018

Minor probability events detection in big data: An integrated approach with Bayesian testing and MIM

Shuo Wan, Jiaxun Lu, Pingyi Fan +1

The minor probability events detection is a crucial problem in Big data. Such events tend to include rarely occurring phenomenons which should be detected and monitored carefully.…