Systems for monitoring air quality are essential for reducing the negative consequences of
air pollution, but creating real-time systems encounters several challenges. The accuracy …

Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the
paradigm of natural language comprehension and generation. Despite their impressive …

Federated Learning (FL) is a promising distributed learning approach that enables multiple
clients to collaboratively train a shared global model. However, recent studies show that FL …

Federated learning (FL) is a privacy-preserving collective machine learning paradigm.
Vertical federated learning (VFL) deals with the case where participants share the same …

The past years have witnessed the success of a distributed learning system called
Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in …

Although federated learning is gaining prevalence in smart sensor networks, substantial
risks to data privacy and security persist. An improper application of federated learning …

Compared with synchronous federated learning (FL), asynchronous FL (AFL) has attracted
more and more attention in edge computing (EC) fields because of its strong adaptability to …

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

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …

Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. Ever-increasing model complexity coupled with limited …