In recent years, federated learning has been widely studied to speed up various\textit
{supervised} learning tasks at the wireless network edge under communication constraints …

In an age defined by explosive growth in information technology, data generation, storage
and transmission have increased dramatically. This data fuels the core of machine learning …

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …

Federated learning (FL) has recently gained much attention due to its effectiveness in
speeding up supervised learning tasks under communication and privacy constraints …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

The paper considers independent reinforcement learning (IRL) for multi-agent collaborative
decision-making in the paradigm of federated learning (FL). However, FL generates …

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

The performance capabilities of models trained in a federated learning (FL) setting over
wireless networks can be significantly affected by the underlying properties of the …

Recently, Over-the-Air (OTA) computation has emerged as a promising federated learning
(FL) paradigm that leverages the waveform superposition properties of the wireless channel …

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 …