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 …

The widespread adoption of consumer electronics devices coupled with the emergence of
6G technology has led to the establishment of an extensive network of interconnected …

Federated learning (FL) is an efficient framework designed to facilitate collaborative model
training across multiple distributed devices while preserving user data privacy. A significant …

Most finger vein authentication systems suffer from the problem of small sample size.
However, the data augmentation can alleviate this problem to a certain extent but did not …

Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …

Federated learning (FL) is a distributed machine learning paradigm that enables a large
number of clients to collaboratively train models without sharing data. However, when the …

Applying sparsity-and overfitting-aware eXtreme Gradient Boosting (XGBoost) for
classification in federated learning allows many participants to train a series of trees …

The incidences of mental health illnesses, such as suicidal ideation and depression, are
increasing, which highlights the urgent need for early detection methods. There is a growing …

Distributedly training models across diverse clients with heterogeneous data samples can
significantly impact the convergence of federated learning. Various novel federated learning …

Federated learning (FL) is a distributed approach to train machine learning models without
disclosing private data from participating clients to a central server. Nevertheless, FL training …