federated learningRiemannian conjugate gradientover-the-air computationIn this paper,we propose a reconfigurable intelligent surface(RIS)assisted over-the-air federated learning(FL),where multiple antennas are deployed at each edge device to enable simultaneous multidimensional model transmission over a ...
在本文中,我们提出了一种新的空中计算方法,通过利用无线多址信道的信号叠加特性,实现ondevice分布式联邦学习的快速全局模型聚合。为了提高设备上分布式学习的统计学习性能和收敛速度,我们提出在满足MSE要求的同时最大化参与全局模型聚合的设备数量,以减少模型聚合误差。这是通过联合器件选择和波束形成设计来实现的,进一步将...
Over-the-air computation for federated learning Mobile Edge Artificial Intelligence Book2022, Mobile Edge Artificial Intelligence Yuanming Shi, ... Yong Zhou Explore book 8.1.2 Over-the-air computation The ever-increasing number of wireless devices such as mobile phones and IoT devices poses great ...
Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient solution to enable distributed machine learning over edge devices by using their data locally to preserve the privacy. By exploiting the waveform superposition property of wireless channels, Air-FEEL allows the ...
Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future, beyond 5G (B5G) and 6G networks. In Air-FEEL, distributed edge devices use their local data to collaboratively train AI models while preserving data ...
As a promising distributed solution, federated learning involves the collaborative model training among edge devices, with the orchestration of a server to carry the capacities of low-latency and privacy preservation for AI -driven networks. To further improve the communication efficiency, over-the-air...
While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating...
In some aspects, a user equipment (UE) may determine quantized parameters in a recurrent neural network (RNN), or gradients to derive the RNN, based at least in part on artificial intelligence (AI) modeling at the UE as part of a federated edge learning system. The UE may generate a ...
over-the-air computationenergy constraintsdynamic schedulingLyapunov optimizationMachine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training framework. As wireless devices involved in FEEL are ...
Federated edge learning (FEEL) has emerged as a revolutionary paradigm for development of AI services at the edge of 6G wireless networks because it supports collaborative model training for a large number of mobile devices. However, model communication over wireless channels, especially in uplink ...