When implementing hierarchical federated learning over wireless networks, scalability
assurance and the ability to handle both interference and device data heterogeneity are …

When implementing hierarchical federated learning over wireless networks, scalability
assurance and the ability to handle both interference and device data heterogeneity are …

The emerging field of federated learning (FL) provides great potential for edge intelligence
while protecting data privacy. However, as the system grows in scale or becomes more …

This paper investigates multiple parallel federated learning in cellular networks, where a
base station schedules several FL tasks in parallel and each task has a group of devices …

We demonstrate that merely analog transmissions and match filtering can realize the
function of an edge server in federated learning (FL). Therefore, a network with massively …

Federated learning (FL) with over-the-air computation can efficiently utilize the
communication bandwidth but is susceptible to analog aggregation error. Excluding those …

Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …

Over-the-air computation is a communication-efficient solution for federated learning (FL). In
such a system, iterative procedure is performed: Local gradient of private loss function is …

Motivated by increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

This article proposes using communication pipelining to enhance the convergence speed of
federated learning in mobile edge computing applications. Due to limited wireless …