Related papers: Federated Learning via Intelligent Reflecting Surf…
This paper studies power-efficient uplink transmission design for federated learning (FL) that employs over-the-air analog aggregation and multi-antenna beamforming at the server. We jointly optimize device transmit weights and receive…
In a practical massive MIMO (multiple-input multiple-output) system, the number of antennas at a base station (BS) is constrained by the space and cost factors, which limits the throughput gain promised by theoretical analysis. This paper…
In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base…
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights)…
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS)…
Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud…
Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global model. FL allows the clients to participate in the training…
Intelligent reflecting surface (IRS) has emerged as a promising technique for wireless communication networks. By dynamically tuning the reflection amplitudes/phase shifts of a large number of passive elements, IRS enables flexible wireless…
In this paper, we propose a hybrid learning framework that combines federated and split learning, termed semi-federated learning (SemiFL), in which over-the-air computation is utilized for gradient aggregation. A key idea is to…
To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of…
To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devices for a shared model training. Specifically, by exploiting…
Intelligent reflecting surface (IRS) has become a promising technology to aid next generation wireless communication systems. In this paper, we develop an alternating beamforming technique with a novel concept of IRS element allocation for…
Intelligent reflecting surface (IRS) is a promising technology to boost the efficiency of wireless energy transfer (WET) systems. However, for a multiuser WET system, simultaneous multi-beam energy transmission is generally required to…
This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a…
Intelligent reflecting surfaces (IRSs) have recently received significant attention for 6G wireless communications as they enable the control of the wireless propagation environment. The use of IRS also provides reducing the hardware…
Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to…
This paper presents a theoretical framework to analyze the performance of integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which IRS provides an additional degree of freedom combined with the…
The idea of Integrated Sensing and Communication (ISAC) offers a promising solution to the problem of spectrum congestion in future wireless networks. This paper studies the integration of intelligent reflective surfaces (IRS) with ISAC…
Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, nonideal communication links and limited transmission resources may hinder the implementation of fast and…
Intelligent Reflecting Surface (IRS) is envisioned to be a promising green and cost-effective solution to enhance wireless network performance by smartly reconfiguring the signal propagation. In this paper, we study an IRS-aided…