Federated learning allows mobile devices, ie, workers, to use their local data to
collaboratively train a global model required by the model owner. Federated learning thus …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …

Federated learning is promising in enabling large-scale machine learning by massive
mobile devices without exposing the raw data of users with strong privacy concerns. Existing …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

With the proliferation of edge intelligence and the breakthroughs in machine learning,
Federated Learning (FL) is capable of learning a shared model across several edge devices …

Federated learning (FL) is an emerging technique used to collaboratively train a machine-
learning model using the data and computation resources of mobile devices without …

We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources for training …

Federated learning (FL) is increasingly considered to circumvent the disclosure of private
data in mobile edge computing (MEC) systems. Training with large data can enhance FL …

Federated learning (FL) is able to manage edge devices to cooperatively train a model while
maintaining the training data local and private. One common assumption in FL is that all …