Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

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 this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …

In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …