70 citations · 94 across the 9 of their papers we have counts for
9 papers
Opportunistic Transmission of Distributed Learning Models in Mobile UAVs
Jingxin Li, Xiaolan Liu, Toktam Mahmoodi
In this paper, we propose an opportunistic scheme for the transmission of model updates from Federated Learning (FL) clients to the server, where clients are wireless mobile users.…
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning
Charuka Herath, Yogachandran Rahulamathavan, Xiaolan Liu
Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to o…
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
Jiadong Yu, Yang Li, Xiaolan Liu +3
By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applicat…
Distributed Intelligence in Wireless Networks
Xiaolan Liu, Jiadong Yu, Yuanwei Liu +4
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wire…
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing
Xiaolan Liu, Jiadong Yu, Yue Gao
To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing arche…
Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning
Xiaolan Liu, Zhijin Qin, Yue Gao
In this paper, we consider resource allocation for edge computing in internet of things (IoT) networks. Specifically, each end device is considered as an agent, which makes its dec…