Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

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

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning
capabilities. Recently, personalized FL (pFL) has received attention for its ability to address …

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy
protection, collaborative learning, and tackling statistical heterogeneity among clients, eg …

Federated Learning (FL), which enables multiple clients to cooperatively train global models
without revealing private data, has gained significant attention from researchers in recent …

Federated learning (FL) provides convenience for cross-domain machine learning
applications and has been widely studied. However, the original FL is still vulnerable to …

Artificial intelligence generated content (AIGC) relies on advanced AI algorithms supported
by extensive datasets and substantial computing power to generate precise and pertinent …

The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the
deployment of intelligent systems, driving the need for innovative data processing …

Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn
increasing attention as the VFL matches the enterprises' demands of leveraging more …