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

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Federated learning (FL) is a promising technology that enables edge devices/clients to
collaboratively and iteratively train a machine learning model under the coordination of a …

The rapid increase in data volume and variety within the field of machine learning
necessitates ethical data utilization and adherence to strict privacy protection standards. Fair …

Federated Learning (FL) is an innovative distributed machine learning paradigm that
enables neural network training across devices without centralizing data. While this …

Time series forecasting plays a critical role in various real-world applications, including
energy consumption prediction, disease transmission monitoring, and weather forecasting …

In the field of machine learning, the rapid development of data volume and variety requires
ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) …

Fairness remains a critical concern in healthcare, where unequal access to services and
treatment outcomes can adversely affect patient health. While Federated Learning (FL) …