Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …

Graph contrastive learning (GCL) enhances unsupervised graph representation by
generating different contrastive views, in which properties of augmented nodes are required …

This paper presents a novel, fast and privacy preserving implementation of deep
autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural …

Federated learning can achieve multi-party data-collaborative applications while
safeguarding personal privacy. However, the process often leads to a decline in the quality …

Federated Learning (FL) allows a global model to be trained without sharing private raw
data. The major challenge in FL is client-wise data heterogeneity leading to different model …

Acquiring big-size datasets to raise the performance of deep models has become one of the
most critical problems in representation learning (RL) techniques, which is the core potential …

Learning-outcome prediction (LOP) is a longstanding and critical problem in educational
routes. Many studies have contributed to developing effective models while often suffering …

Federated learning shows promise as a privacy-preserving collaborative learning technique.
Existing research mainly focuses on skewing the class distribution across clients. However …

Nowadays, online examination (exam in short) platforms are becoming more popular,
demanding strong security measures for digital learning environments. This includes …

The personalization of machine learning (ML) models to address data drift is a significant
challenge in the context of Internet of Things (IoT) applications. Presently, most approaches …