The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …

Most existing federated learning algorithms are based on the vanilla FedAvg scheme.
However, with the increase of data complexity and the number of model parameters, the …

Federated learning allows multiple parties to train models while jointly protecting user
privacy. However, traditional federated learning requires each client to have the same model …

Federated Learning (FL) received widespread attention in 5G mobile edge networks (MENs)
as it enables collaborative training deep learning models without disclosing users' private …

Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Generative Adversarial Networks (GANs) have received a lot of attention due to their
powerful generative ability, and many related studies have been carried out. Among them …

Federated learning (FL) emerged as a novel machine learning setting that enables
collaboratively training deep models on decentralized clients with privacy constraints. In …

Applying knowledge distillation (KD) in federated learning (FL) can transfer model
knowledge between clients' local models and global model, which helps to improve the …

Due to the distributed storage of massive data, efficient deployment of Generative
Adversarial Networks (GANs) in distributed scenarios has become a hot topic. This paper …