Federated learning (FL) has been widely deployed in edge computing scenarios. However,
FL-related technologies are still facing severe challenges while evolving rapidly. Among …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

Pre-training is prevalent in nowadays deep learning to improve the learned model's
performance. However, in the literature on federated learning (FL), neural networks are …

Federated learning (FL) has recently attracted increasing attention from academia and
industry, with the ultimate goal of achieving collaborative training under privacy and …

Federated learning enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT devices. However …

Federated learning (FL) is a machine learning approach where the goal is to train a
centralized model using a large number of clients that host private datasets. FL trains a …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

In federated learning, all networked clients contribute to the model training cooperatively.
However, with model sizes increasing, even sharing the trained partial models often leads to …

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the
generalization of the global model on each client. To address this, we propose a method …

Federated learning (FL) has emerged as a prominent approach for collaborative training of
machine learning models across distributed clients while preserving data privacy. However …