… We propose an auction based market model to facilitate commercializing federated learning
… In this paper, we mainly investigate the federated learning in the wireless communication …

… communication steps on the convergence of the proposed algorithm. We prove, in practice,
federated learning … employed for if the unreliability of wireless channels is neglected. Finally, …

… We highlight the major issues and recent advancements in the subjects of wireless
communication and healthcare. Finally, we demonstrate the promise of FL approaches in healthcare …

… Abstract—Due to the dynamics of wireless channels and limited wireless resources (ie,
spectrum), deploying federated learning (FL) over wireless networks is challenged by frequent …

… Abstract—Federated learning (FL) is a popular framework for training an AI model using …
mobile data in a wireless network. It features data parallelism by distributing the learning task to …

Federated learning (FL) rests on the notion of training a global model in a decentralized
manner. Under this setting, mobile devices perform computations on their local data before …

In federated learning (FL), reducing the communication … share the training parameters over
wireless links. With such … local processing and communication parameters (including the …

… Abstract—In this paper, we consider federated learning in wireless edge networks. Transmitting
stochastic gradients (SG) or deep model’s parameters over a limited-bandwidth wireless …

… The work in [23] aimed at minimizing the global FL loss function under the effect of wireless
communications. However, the computation delay of the local FL training model was not …

… , new distributed machine learning (ML) methods have emerged. In the wireless community,
Federated Learning (FL) is of particular interest due to its communication efficiency and its …