The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …

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 …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively
learn a global model by sharing local updates on the model parameters rather than their …

Semantic communications represent a significant breakthrough with respect to the current
communication paradigm, as they focus on recovering the meaning behind the transmitted …

The development of emerging applications, such as autonomous transportation systems, is
expected to result in an explosive growth in mobile data traffic. As the available spectrum …

Existing communication systems are mainly built based on Shannon's information theory,
which deliberately ignores the semantic aspects of communication. The recent iteration of …

Existing deep learning-enabled semantic communication systems often rely on shared
background knowledge between the transmitter and receiver that includes empirical data …

Wireless connectivity has traditionally been regarded as an opaque data pipe carrying
messages, whose context-dependent meaning and effectiveness have been ignored …