no code implementations • 15 Aug 2022 • Liang Li, Chenpei Huang, Dian Shi, Hao Wang, Xiangwei Zhou, Minglei Shu, Miao Pan
Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i. e., iterative local computing + multi-round communications) of mobile devices in FL.
no code implementations • 19 May 2022 • Rui Chen, Dian Shi, Xiaoqi Qin, Dongjie Liu, Miao Pan, Shuguang Cui
In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices.
no code implementations • 13 Jan 2021 • Dian Shi, Liang Li, Rui Chen, Pavana Prakash, Miao Pan, Yuguang Fang
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications.
no code implementations • 22 Dec 2020 • Liang Li, Dian Shi, Ronghui Hou, Hui Li, Miao Pan, Zhu Han
Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications.