One big challenge to federated learning is the non-IID data distribution caused by
imbalanced classes. Existing federated learning approaches tend to bias towards classes …

Asynchronous federated learning (AFL) is a distributed machine learning technique that
allows multiple devices to collaboratively train deep learning models without sharing local …

In the last decade, the explosive surge in multi-modal data has propelled cross-modal
retrieval into the forefront of information retrieval research. Exceptional cross-modal retrieval …

As a key task in graph data mining, graph link prediction plays a vital role across various
downstream applications, including e-commerce recommendations, protein interaction fore …

Federated learning enables a collaborative training and optimization of global models
among a group of devices without sharing local data samples. However, the heterogeneity …

We propose a straightforward solution for detecting scarce topics in unbalanced short-text
datasets. Our approach, named CWUTM (Topic model based on co-occurrence word …