129 citations · 178 across the 14 of their papers we have counts for
14 papers
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning
Shuo Wan, Jiaxun Lu, Pingyi Fan +3
Federated learning (FL) has recently emerged as a transformative paradigm that jointly train a model with distributed data sets in IoT while avoiding the need for central data coll…
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…
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
Zheqi Zhu, Shuo Wan, Pingyi Fan +1
As an emerging technique, mobile edge computing (MEC) introduces a new processing scheme for various distributed communication-computing systems such as industrial Internet of Thin…
An Importance Aware Weighted Coding Theorem Using Message Importance Measure
Zheqi Zhu, Shanyun Liu, Rui She +3
There are numerous scenarios in source coding where not only the code length but the importance of each value should also be taken into account. Different from the traditional codi…
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…
Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges
Rui She, Shanyun Liu, Shuo Wan +2
In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information of big data collected by Internet…