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Xiaolan Liu - IEEE Xplore Author Profile
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Xiaolan Liu

Publications
37
Citations
692
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (47)
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Xiaolan Liu

Affiliation

Smart Internet Laboratory
University of Bristol
Bristol, U.K.

Publication Topics

Biography

Xiaolan Liu (Member, IEEE) received the Ph.D. degree from the Queen Mary University of London, U.K., in 2021. She was a Research Associate with King’s College London from 2020 to 2021. Since October 2021, she has been with the Institute for Digital Technologies, Loughborough University London, U.K., where she is currently a Lecturer. Her current research interests include wireless distributed learning, multiagent reinforcement learning for edge computing, and machine learning for wireless communication optimization.(Based on document published on 12 April 2023).
Publications
37
Citations
692
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (47)
Author's Published Works

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Maximizing Uncertainty for Federated Learning via Bayesian Optimization-Based Model Poisoning

Marios Aristodemou;Xiaolan Liu;Yuan Wang;Konstantinos G. Kyriakopoulos;Sangarapillai Lambotharan;Qingsong Wei

IEEE Transactions on Information Forensics and Security
Year: 2025 | Volume: 20 | Journal Article |
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine learning (ML) technology. A common denominator for the metrics of trustworthiness is the quantification of uncertainty inherent in DL algorithms, and specifically in the model parameters, input data, and model prediction...Show More

Maximizing Uncertainty for Federated Learning via Bayesian Optimization-Based Model Poisoning

Marios Aristodemou;Xiaolan Liu;Yuan Wang;Konstantinos G. Kyriakopoulos;Sangarapillai Lambotharan;Qingsong Wei

Year: 2025 | Volume: 20 | Journal Article |

Enhancing Federated Learning Convergence With Dynamic Data Queue and Data-Entropy-Driven Participant Selection

Charuka Herath;Xiaolan Liu;Sangarapillai Lambotharan;Yogachandran Rahulamathavan

IEEE Internet of Things Journal
Year: 2025 | Volume: 12, Issue: 6 | Journal Article |
Federated learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost efficiency. Our emphasis in this research lies in addressing statistical complexity in FL, especially when the data stored locally across devices is not identically and independently d...Show More

Enhancing Federated Learning Convergence With Dynamic Data Queue and Data-Entropy-Driven Participant Selection

Charuka Herath;Xiaolan Liu;Sangarapillai Lambotharan;Yogachandran Rahulamathavan

Year: 2025 | Volume: 12, Issue: 6 | Journal Article |

Bayesian Optimisation-Driven Poisoning Attack Against Personalised Federated Learning in Metaverse

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan

2024 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2024 | Conference Paper |
Metaverse is envisioned to be a human-centric framework that aims to provide a comprehensive and immersive experience for users in various domains, including education, medicine, and entertainment. As a large amount of private data is generated from each user, federated learning (FL) has emerged as an effective solution to ensure user privacy. Moreover, personalized FL (PFL) was further studied to...Show More

Bayesian Optimisation-Driven Poisoning Attack Against Personalised Federated Learning in Metaverse

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan

Year: 2024 | Conference Paper |

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Jianing Zheng;Xiaolan Liu;Zhuang Ling;Fengye Hu

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 18 | Journal Article |
With the advent of the 5G and 6G eras and the explosive growth of mobile users, machine learning (ML) is increasingly used for extracting important information from a large amount of generated data and making intelligent decisions for complex environments. Especially, distributed ML techniques are getting more attention to enable training ML models in a distributed manner by exploiting distributed...Show More

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Jianing Zheng;Xiaolan Liu;Zhuang Ling;Fengye Hu

Year: 2024 | Volume: 11, Issue: 18 | Journal Article |

Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction

Jingxin Li;Xiaolan Liu;Toktam Mahmoodi

IEEE Open Journal of the Communications Society
Year: 2024 | Volume: 5 | Journal Article |
Cited by: Papers (2)
Despite the recent advancements achieved by federated learning (FL), its real-world deployment is significantly impeded by the heterogeneous learning environment, specifically manifesting as devices with various computing capabilities, non-I.I.D. (Independent Identically and Distributed) data distribution and dynamic wireless transmission conditions. Such learning heterogeneity greatly harms the l...Show More

Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction

Jingxin Li;Xiaolan Liu;Toktam Mahmoodi

Year: 2024 | Volume: 5 | Journal Article |

Asynchronous Personalized Learning for Heterogeneous Wireless Networks

Xiaolan Liu;Jackson Ross;Yue Liu;Yuanwei Liu

2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Year: 2023 | Conference Paper |
The future wireless networks are expected to support more artificial intelligence (AI)-enabled applications, such as Metaverse services, at the network edge. The AI algorithms, like deep learning, play an important role in extracting important information from a large dataset, but conventional centralized learning requires collecting the datasets that are distributed over the users and always incl...Show More

Asynchronous Personalized Learning for Heterogeneous Wireless Networks

Xiaolan Liu;Jackson Ross;Yue Liu;Yuanwei Liu

Year: 2023 | Conference Paper |

Opportunistic Transmission of Distributed Learning Models in Mobile UAVs

Jingxin Li;Xiaolan Liu;Toktam Mahmoodi

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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. This proposal aims to opportunistically take advantage of the proximity of users to the base station or the general condition of the wireless transmission channel, rather than traditional synchronous transmission. In th...Show More

Opportunistic Transmission of Distributed Learning Models in Mobile UAVs

Jingxin Li;Xiaolan Liu;Toktam Mahmoodi

Year: 2023 | Conference Paper |

Energy Efficient IRS Assisted NOMA Aided Mobile Edge Computing via Heterogeneous Multi-Agent Reinforcement Learning

Jiadong Yu;Yang Li;Xiaolan Liu;Bo Sun;Yuan Wu;Danny H.K. Tsang

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) system can enhance the spectral-efficiency with massive tasks offloading. However, with more dynamic devices and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly adjust the communication environment ...Show More

Energy Efficient IRS Assisted NOMA Aided Mobile Edge Computing via Heterogeneous Multi-Agent Reinforcement Learning

Jiadong Yu;Yang Li;Xiaolan Liu;Bo Sun;Yuan Wu;Danny H.K. Tsang

Year: 2023 | Conference Paper |

Adversarial Poisoning Attacks on Federated Learning in Metaverse

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Metaverse is envisioned to be a human-centric framework, and provide a new concept of living by offering comprehensively immersive experience for users in education, medicine and entertainment domain. Since a large amount of private data is generated at each user for accessing Metaverse, the emerging federated learning (FL) provides an effective solution to address the potential privacy leakage of...Show More

Adversarial Poisoning Attacks on Federated Learning in Metaverse

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan

Year: 2023 | Conference Paper |

Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan;Basil AsSadhan

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (1)
Metaverse is envisioned to be the next-generation human-centric Internet which can offer an immersive experience for users with a broad application in healthcare, education, entertainment, and industries. These applications require the analysis of massive data that contains private and sensitive information. A potential solution to preserving privacy is deploying distributed learning frameworks, i...Show More

Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning

Marios Aristodemou;Xiaolan Liu;Sangarapillai Lambotharan;Basil AsSadhan

Year: 2023 | Volume: 11 | Journal Article |

Recursive Euclidean Distance-based Robust Aggregation Technique for Federated Learning

Charuka Herath;Yogachandran Rahulamathavan;Xiaolan Liu

2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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 obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by ...Show More

Recursive Euclidean Distance-based Robust Aggregation Technique for Federated Learning

Charuka Herath;Yogachandran Rahulamathavan;Xiaolan Liu

Year: 2023 | Conference Paper |

Distributed Learning for Metaverse over Wireless Networks

Xiaolan Liu;Yuanwei Liu

IEEE Communications Magazine
Year: 2023 | Volume: 61, Issue: 9 | Magazine Article |
Cited by: Papers (5)
Metaverse is envisioned to be a human-centric framework that creates an interface for users to immerse themselves in education, professional training, and entertainment by accessing a virtual world. The quality of immersive experiences (QoIE) naturally comes out as a metric to measure the multi-sensory multimedia (MSMM) communication provided by Metaverse networks, we first propose a human-centric...Show More

Distributed Learning for Metaverse over Wireless Networks

Xiaolan Liu;Yuanwei Liu

Year: 2023 | Volume: 61, Issue: 9 | Magazine Article |

Distributed Intelligence in Wireless Networks

Xiaolan Liu;Jiadong Yu;Yuanwei Liu;Yue Gao;Toktam Mahmoodi;Sangarapillai Lambotharan;Danny Hin-Kwok Tsang

IEEE Open Journal of the Communications Society
Year: 2023 | Volume: 4 | Journal Article |
Cited by: Papers (12)
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers c...Show More

Distributed Intelligence in Wireless Networks

Xiaolan Liu;Jiadong Yu;Yuanwei Liu;Yue Gao;Toktam Mahmoodi;Sangarapillai Lambotharan;Danny Hin-Kwok Tsang

Year: 2023 | Volume: 4 | Journal Article |

IRS Assisted NOMA Aided Mobile Edge Computing With Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning

Jiadong Yu;Yang Li;Xiaolan Liu;Bo Sun;Yuan Wu;Danny Hin-Kwok Tsang

IEEE Transactions on Wireless Communications
Year: 2023 | Volume: 22, Issue: 7 | Journal Article |
Cited by: Papers (36)
By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable st...Show More

IRS Assisted NOMA Aided Mobile Edge Computing With Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning

Jiadong Yu;Yang Li;Xiaolan Liu;Bo Sun;Yuan Wu;Danny Hin-Kwok Tsang

Year: 2023 | Volume: 22, Issue: 7 | Journal Article |

Wireless Distributed Learning: A New Hybrid Split and Federated Learning Approach

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

IEEE Transactions on Wireless Communications
Year: 2023 | Volume: 22, Issue: 4 | Journal Article |
Cited by: Papers (42)
Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performe...Show More

Wireless Distributed Learning: A New Hybrid Split and Federated Learning Approach

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

Year: 2023 | Volume: 22, Issue: 4 | Journal Article |

Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

ICC 2022 - IEEE International Conference on Communications
Year: 2022 | Conference Paper |
Cited by: Papers (18)
The use of unmanned aerial vehicles (UAVs) as flying users provides various applications by exploiting machine learning (ML) algorithms. Recently, distributed learning algorithms, federated learning (FL) and split learning (SL), have been exploited to train ML models distributedly via sharing model parameters rather than large raw datasets in the conventional centralized learning algorithms. Consi...Show More

Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

Year: 2022 | Conference Paper |

A Novel Hybrid Split and Federated Learning Architecture in Wireless UAV Networks

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

ICC 2022 - IEEE International Conference on Communications
Year: 2022 | Conference Paper |
Cited by: Papers (8)
The ever-growing use of unmanned aerial vehicles (UAVs) as aerial users is becoming a major part of the sixth generation (6G) networks, which could provide various applications, like object detection and video surveillance, by exploiting machine learning (ML) algorithms. However, the training of conventional centralized ML algorithms causes high communication overhead due to the transmission of la...Show More

A Novel Hybrid Split and Federated Learning Architecture in Wireless UAV Networks

Xiaolan Liu;Yansha Deng;Toktam Mahmoodi

Year: 2022 | Conference Paper |

Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

Jiadong Yu;Xiaolan Liu;Yue Gao;Chiya Zhang;Wei Zhang

IEEE Transactions on Wireless Communications
Year: 2022 | Volume: 21, Issue: 9 | Journal Article |
Cited by: Papers (39)
To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees a...Show More

Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

Jiadong Yu;Xiaolan Liu;Yue Gao;Chiya Zhang;Wei Zhang

Year: 2022 | Volume: 21, Issue: 9 | Journal Article |

3D On and Off-Grid Dynamic Channel Tracking for Multiple UAVs and Satellite Communications

Jiadong Yu;Xiaolan Liu;Yue Gao;Xuemin Shen

IEEE Transactions on Wireless Communications
Year: 2022 | Volume: 21, Issue: 6 | Journal Article |
Cited by: Papers (6)
The space-air-ground integrated network (SAGIN) has drawn increasing attention for its benefits, such as wide coverage, high throughput for 5G and 6G communications. As one of the links, space-air communications between multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellites face a crucial challenge in tracking the 3D dynamic channel information. This paper e...Show More

3D On and Off-Grid Dynamic Channel Tracking for Multiple UAVs and Satellite Communications

Jiadong Yu;Xiaolan Liu;Yue Gao;Xuemin Shen

Year: 2022 | Volume: 21, Issue: 6 | Journal Article |

On-Grid 3D Dynamic Channel Tracking for Space-Air Communications with Multiple UAVs

Jiadong Yu;Xiaolan Liu;Yue Gao

2021 IEEE/CIC International Conference on Communications in China (ICCC)
Year: 2021 | Conference Paper |
The space-air-ground integrated network (SAGIN) has drawn increasing attention for its potential to support ubiquitous wireless communications. As one of the link segments, it is non-trivial to track the 3D dynamic channel information in space-air links with multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellite. In this paper, we proposed a multi-dimensional...Show More

On-Grid 3D Dynamic Channel Tracking for Space-Air Communications with Multiple UAVs

Jiadong Yu;Xiaolan Liu;Yue Gao

Year: 2021 | Conference Paper |

Long-Term Channel Statistic Estimation for Highly-Mobile Hybrid MmWave Multi-User MIMO Systems

Jiadong Yu;Xiaolan Liu;Haoran Qi;Yue Gao

IEEE Transactions on Vehicular Technology
Year: 2020 | Volume: 69, Issue: 12 | Journal Article |
Cited by: Papers (13)
Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information static. However, due to the Doppler effect caused by the mobility of users, such as unmanned aerial vehicles, high-speed trains and autonomous vehicles, the mmWave channel is changing rapidly. Spatial channel covariance, defi...Show More

Long-Term Channel Statistic Estimation for Highly-Mobile Hybrid MmWave Multi-User MIMO Systems

Jiadong Yu;Xiaolan Liu;Haoran Qi;Yue Gao

Year: 2020 | Volume: 69, Issue: 12 | Journal Article |

3D Channel Tracking for UAV-Satellite Communications in Space-Air-Ground Integrated Networks

Jiadong Yu;Xiaolan Liu;Yue Gao;Xuemin Shen

IEEE Journal on Selected Areas in Communications
Year: 2020 | Volume: 38, Issue: 12 | Journal Article |
Cited by: Papers (51)
The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) sate...Show More

3D Channel Tracking for UAV-Satellite Communications in Space-Air-Ground Integrated Networks

Jiadong Yu;Xiaolan Liu;Yue Gao;Xuemin Shen

Year: 2020 | Volume: 38, Issue: 12 | Journal Article |

Fairness-Aware Intragroup Cooperative Transmission in Wireless Powered Communication Networks

Zhi Mao;Fengye Hu;Dayang Sun;Shaodan Ma;Xiaolan Liu

IEEE Transactions on Vehicular Technology
Year: 2020 | Volume: 69, Issue: 6 | Journal Article |
Cited by: Papers (10)
This article investigates fairness-aware transmission in a wireless powered communication network (WPCN), in which a group of adjacent users receive radio frequency energy and then cooperatively transmit information to a remote access point (AP) by using the harvested energy with time division multiple access (TDMA) scheme. Considering the constraints of practical energy harvesting electric circui...Show More

Fairness-Aware Intragroup Cooperative Transmission in Wireless Powered Communication Networks

Zhi Mao;Fengye Hu;Dayang Sun;Shaodan Ma;Xiaolan Liu

Year: 2020 | Volume: 69, Issue: 6 | Journal Article |

Learning to Predict the Mobility of Users in Mobile mmWave Networks

Xiaolan Liu;Jiadong Yu;Haoran Qi;Jianxin Yang;Wenge Rong;Xiuyin Zhang;Yue Gao

IEEE Wireless Communications
Year: 2020 | Volume: 27, Issue: 1 | Magazine Article |
Cited by: Papers (21)
MmWave communication suffers from severe path loss due to high frequency and is sensitive to blockages because of high penetration loss, especially in mobile communication scenarios. It highly depends on line-of-sight channels and narrow beams, and thus efficient beam tracking and beam alignment are necessary techniques to maintain robust communication links, in which tracking user mobility lays t...Show More

Learning to Predict the Mobility of Users in Mobile mmWave Networks

Xiaolan Liu;Jiadong Yu;Haoran Qi;Jianxin Yang;Wenge Rong;Xiuyin Zhang;Yue Gao

Year: 2020 | Volume: 27, Issue: 1 | Magazine Article |

Resource Allocation With Edge Computing in IoT Networks via Machine Learning

Xiaolan Liu;Jiadong Yu;Jian Wang;Yue Gao

IEEE Internet of Things Journal
Year: 2020 | Volume: 7, Issue: 4 | Journal Article |
Cited by: Papers (112)
In this article, we investigate resource allocation with edge computing in Internet-of-Things (IoT) networks via machine learning approaches. Edge computing is playing a promising role in IoT networks by providing computing capabilities close to users. However, the massive number of users in IoT networks requires sufficient spectrum resource to transmit their computation tasks to an edge server, w...Show More

Resource Allocation With Edge Computing in IoT Networks via Machine Learning

Xiaolan Liu;Jiadong Yu;Jian Wang;Yue Gao

Year: 2020 | Volume: 7, Issue: 4 | Journal Article |

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