Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

Abstract Model heterogeneous federated learning is a challenging task since each client
independently designs its own model. Due to the annotation difficulty and free-riding …

Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

This work considers the category distribution heterogeneity in federated learning. This issue
is due to biased labeling preferences at multiple clients and is a typical setting of data …

While" instruction-tuned" generative large language models (LLMs) have demonstrated an
impressive ability to generalize to new tasks, the training phases heavily rely on large …

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 …