Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

We consider federated learning (FL) with multiple wireless edge servers having their own
local coverage. We focus on speeding up training in this increasingly practical setup. Our …

Federated learning (FL) enables collaborative model training without centralizing data.
However, the traditional FL framework is cloud-based and suffers from high communication …

Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

Federated learning (FL) can generate huge communication overhead for the central server,
which may cause operational challenges. Furthermore, the central server's failure or …

Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …

Federated Learning (FL) enables the distributed machine learning (ML) without violating the
privacy of local users. In the scenario wireless FL, it is challenging for some local clients to …

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 …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …