Federated Learning (FL) is a machine learning paradigm where many clients collaboratively
learn a shared global model with decentralized training data. Personalized FL additionally …

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) is an effective machine learning paradigm where multiple clients
can train models based on heterogeneous data in a decentralized manner without …

Federated Learning (FL) is an emerging paradigm that enables distributed users to
collaboratively and iteratively train machine learning models without sharing their private …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
learn a powerful global model by aggregating local training. However, the performance of …

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated
learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
collaboratively learn a shared global model. Despite the recent progress, it remains …

Most cross-device federated learning (FL) studies focus on the model-homogeneous setting
where the global server model and local client models are identical. However, such …

Data heterogeneity is one of the most challenging issues in federated learning, which
motivates a variety of approaches to learn personalized models for participating clients. One …

Federated learning (FL) allows multiple clients to collaboratively train a deep learning
model. One major challenge of FL is when data distribution is heterogeneous, ie, differs from …