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pro vyhledávání: '"Liu, Xiaolan"'
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of communication approaches in MARL. While multiple communication method
Externí odkaz:
http://arxiv.org/abs/2401.15059
Autor:
Xu, Yaohui, Liu, Xiaolan, Li, Wujun, Wang, Haotian, Guo, Jun, Ma, Jie, Zhang, Jianing, Shen, Deyuan
We demonstrate laser frequency stabilization using a high-Q MgF2 crystalline whispering gallery mode resonator coupled with a tapered fiber. We discovered that the tapered fiber, acting as a microcantilever, exhibits mechanical resonance characterist
Externí odkaz:
http://arxiv.org/abs/2312.02569
Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the context of intelligent vehicle control, RL has made impressive progress
Externí odkaz:
http://arxiv.org/abs/2311.02746
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 proximi
Externí odkaz:
http://arxiv.org/abs/2306.09484
Autor:
Rahulamathavan, Yogachandran, Herath, Charuka, Liu, Xiaolan, Lambotharan, Sangarapillai, Maple, Carsten
The federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. While FL ensures that a user's data always remain with the user, the gradients are shared with the centralized server to
Externí odkaz:
http://arxiv.org/abs/2306.05112
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
Externí odkaz:
http://arxiv.org/abs/2303.11337
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
Externí odkaz:
http://arxiv.org/abs/2209.09776
Autor:
Liu, Xiaolan, Yu, Jiadong, Liu, Yuanwei, Gao, Yue, Mahmoodi, Toktam, Lambotharan, Sangarapillai, Tsang, Danny H. K.
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 wireless devices and typically zillions bytes of data they produce at the
Externí odkaz:
http://arxiv.org/abs/2208.00545
Autor:
Lyu, Xin, Yang, Tong, Lan, Tianxiang, Wang, Yongdong, Liu, Xiaolan, Wang, Yongtian, Cheng, Dewen
Publikováno v:
In Optics and Lasers in Engineering August 2024 179
Publikováno v:
In Journal of Functional Foods June 2024 117