no code implementations • 23 Apr 2024 • Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, Qiang Yang
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.
no code implementations • 14 Mar 2024 • Yulan Gao, Chao Ren, Han Yu
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited.
no code implementations • 19 Jan 2024 • Ziqiaing Ye, Yulan Gao, Yue Xiao, Zehui Xiong, Dusit Niyato
In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities.
no code implementations • 7 Aug 2023 • Yulan Gao, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Han Yu
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners.
no code implementations • 19 Jul 2023 • Yulan Gao, Ziqiang Ye, Yue Xiao, Wei Xiang
These methods are designed to minimize the cost incurred by the worst-case participant and ensure the long-term fairness of FL in hierarchical Internet of Things (HieIoT) networks.
no code implementations • 26 Jun 2023 • Ziqiang Ye, Yulan Gao, Yue Xiao, Minrui Xu, Han Yu, Dusit Niyato
We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks.
no code implementations • 11 May 2023 • Yulan Gao, Yansong Zhao, Han Yu
However, the problem of optimizing FL client selection in mobile federated learning networks (MFLNs), where devices move in and out of each others' coverage and no FL server knows all the data owners, remains open.
no code implementations • 5 Nov 2022 • Mingming Wu, Yue Xiao, Yulan Gao, Ming Xiao
A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications.
no code implementations • 1 Nov 2022 • Yulan Gao, Ziqiang Ye, Han Yu, Zehui Xiong, Yue Xiao, Dusit Niyato
This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system.
no code implementations • 1 Nov 2022 • Yulan Gao, Yue Xiao, Xianfu Lei, Qiaonan Zhu, Dusit Niyato, Kai-Kit Wong, Pingzhi Fan, Rose Qingyang Hu
Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs.
no code implementations • 18 Dec 2020 • Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao
This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).