no code implementations • 27 May 2024 • Jiawei Shao, Jingwen Tong, Qiong Wu, Wei Guo, Zijian Li, Zehong Lin, Jun Zhang
To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
no code implementations • 29 Jan 2024 • Wenqiang Sun, Teng Li, Zehong Lin, Jun Zhang
Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.
no code implementations • 30 Aug 2023 • Zijian Li, Zehong Lin, Jiawei Shao, Yuyi Mao, Jun Zhang
However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.
no code implementations • 20 Jul 2023 • Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou, Yuchang Sun, Lumin Liu, Zehong Lin, Yuyi Mao, Jun Zhang
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
no code implementations • 6 Jul 2023 • Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng Li, Jun Zhang, Khaled B. Letaief
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
no code implementations • 26 May 2023 • Yuchang Sun, Zehong Lin, Yuyi Mao, Shi Jin, Jun Zhang
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
no code implementations • 13 May 2023 • Tailin Zhou, Zehong Lin, Jun Zhang, Danny H. K. Tsang
Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models.
no code implementations • 26 Jul 2022 • Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.
no code implementations • 6 Sep 2021 • Hang Liu, Zehong Lin, Xiaojun Yuan, Ying-Jun Angela Zhang
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.
1 code implementation • 20 Jul 2021 • Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large.