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 ...
Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-...
Over-the-Air Federated Edge Learning with Hierarchical Clustering We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile users (MUs) aim to reach a consensus on a global model with the help o... Aygün, Ozan,Kazemi, Mohammad,Gündüz, Deniz,... - arXiv e...
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...
Future research will include the development of resilient federated learning algorithms when there are malicious agents in the system. REFERENCES [1] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentra...
Over-the-air computation for federated learning YuanmingShi, ...YongZhou, inMobile Edge Artificial Intelligence, 2022 8.1.2Over-the-air computation The ever-increasing number of wireless devices such as mobile phones and IoT devices poses great challenge for the 5G system. To address this issue,...
Embodiments of the present invention may provide the capability to personalize end devices over-the-air (OTA) without the involvement of device manufacturers, for example, in a federated large scale wireless IoT network, such as LoRaWAN. Preset with factory settings, end devices may securely connec...
Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for privacy-preserving distributed learning over wireless networks. Air-FEEL allows "one-shot" over-the-air aggregation of gradient/model-updates...
The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose...
This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system. Different from conventional power control designs (e.g., to minimize the individual mean squared error (MSE) of the over-the-air aggregation at each round), we consider a new powe...