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pro vyhledávání: '"Fang, Wenzhi"'
While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibit hierarchical architectures. Hierarchical FL (HFL) has emerged as a p
Externí odkaz:
http://arxiv.org/abs/2409.18448
Autor:
Han, Dong-Jun, Fang, Wenzhi, Hosseinalipour, Seyyedali, Chiang, Mung, Brinton, Christopher G.
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning se
Externí odkaz:
http://arxiv.org/abs/2408.09522
Hierarchical federated learning (HFL) has demonstrated promising scalability advantages over the traditional "star-topology" architecture-based federated learning (FL). However, HFL still imposes significant computation, communication, and storage bu
Externí odkaz:
http://arxiv.org/abs/2310.17890
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have bee
Externí odkaz:
http://arxiv.org/abs/2201.09531
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp i
Externí odkaz:
http://arxiv.org/abs/2105.05113
In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple
Externí odkaz:
http://arxiv.org/abs/2105.05024
Over-the-air computation (AirComp) is a promising technology that is capable of achieving fast data aggregation in Internet of Things (IoT) networks. The mean-squared error (MSE) performance of AirComp is bottlenecked by the unfavorable channel condi
Externí odkaz:
http://arxiv.org/abs/2005.10625
Intelligent reflecting surface (IRS) has the potential to significantly enhance the network performance by reconfiguring the wireless propagation environments. It is however difficult to obtain the accurate downlink channel state information (CSI) fo
Externí odkaz:
http://arxiv.org/abs/2005.07416
Autor:
Chen, Boru, Dang, Leping, Zhang, Xiao, Fang, Wenzhi, Hou, Mengna, Liu, Tiankuo, Wang, Zhanzhong
Publikováno v:
In Food Chemistry 15 March 2017 219:93-101
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