Federated learning (FL) collaboratively trains a shared global model depending on multiple
local clients, while keeping the training data decentralized to preserve data privacy …

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 …

Federated Learning (FL) aims to learn a global model from distributed users while protecting
their privacy. However, when data are distributed heterogeneously the learning process …

Federated Learning (FL) has emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

Federated learning has become a popular method to learn from decentralized
heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models …

Federated learning (FL) is an emerging technique that trains massive and geographically
distributed edge data while maintaining privacy. However, FL has inherent challenges in …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

The federated learning (FL) paradigm emerges to preserve data privacy during model
training by only exposing clients' model parameters rather than original data. One of the …

Training Deep Learning (DL) models require large, high-quality datasets, often assembled
with data from different institutions. Federated Learning (FL) has been emerging as a …