Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …

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 …

Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning (FL) strives to enable collaborative training of machine learning models
without centrally collecting clients' private data. Different from centralized training, the local …

With growing concerns regarding data privacy and rapid increase in data volume, Federated
Learning (FL) has become an important learning paradigm. However, jointly learning a deep …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …