Related papers: Cell-Free Massive MIMO for Wireless Federated Lear…

Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. However, the learning accuracy decreases due to the heterogeneity of devices' data, and the computation and communication latency…

Constrained by weak signal strength and significant inter-cell interference, users located at the cell edge in a cellular network suffer from inferior service quality. Recently, cell-free massive MIMO (CFmMIMO) has gained considerable…

Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federated learning (FL) enables multiple clients to collaboratively…

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user…

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some…

This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning by accounting for both computational and communication…

Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for…

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models…

The impressive growth of wireless data networks has recently led to increased attention to the issue of electromagnetic pollution. Specific absorption rates and incident power densities have become popular indicators for measuring…

Large-scale federated learning (FL) over wireless multiple access channels (MACs) has emerged as a crucial learning paradigm with a wide range of applications. However, its widespread adoption is hindered by several major challenges,…

Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet companies have deployed FL in their applications for specific…

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced…

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE…

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous training activities could overload resource-constrained devices.…

Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity) of the corresponding transmitter. Federated learning (FL) has emerged as a popular…

We investigate a fronthaul-limited cell-free massive multiple-input multiple-output (CF-mMIMO) system and propose a hybrid centralized-distributed precoding strategy that dynamically adapts to varying fronthaul and spectral efficiency (SE)…

Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) effectively extracts data by decoding interference, yet its…

Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which…

Due to the relatively high cost of deploying optical fibers, free space optical (FSO) links have been developed as a cost-effective alternative technology for next generation cellular networks. However, in order to have a practical role in…

Cell-Free (CF) Massive MIMO (mMIMO) is a technology which can potentially augment not only the deployment of 5G, but also the deployment of beyond 5G (B5G) wireless networks. However, the cost for rolling out such systems may be…