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

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

This paper studies federated learning (FL) in a classic wireless network, where learning
clients share a common wireless link to a coordinating server to perform federated model …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

Although valued for its ability to allow teams to collaborate and foster coalitional behaviors
among the participants, game theory's application to networking systems is not without …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

In distributed optimization and machine learning, multiple nodes coordinate to solve large
problems. To do this, the nodes need to compress important algorithm information to bits so …

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …