2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.DC2024
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang +4
As an emerging technology, digital twin (DT) can provide real-time status and dynamic topology mapping for Internet of Things (IoT) devices. However, DT and its implementation with…
cs.GT2023★ 2 cited
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Shunfeng Chu, Jun Li, Kang Wei +4
Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' mode…
cs.DC2021
Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling
Shunfeng Chu, Jun Li, Jianxin Wang +5
With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and sends them to a central server for aggregation, the…