Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

There are two categories of methods in Federated Learning (FL) for joint training across
multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) …

The inevitable private information in legal data necessitates legal artificial intelligence to
study privacy-preserving and decentralized learning methods. Federated learning (FL) has …

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …

Traditional Federated Learning (FL) follows a server-domincated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

The metaverse, which is at the stage of innovation and exploration, faces the dilemma of
data collection and the problem of private data leakage in the process of development. This …

Federated Learning is a promising technique that facilitates cloud–edge–terminal
collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without …

The development of Internet of Things (IoT) makes human life more intelligent, and the
interconnection of all things has become a reality. However, the surge in the number of …

Federated learning (FL) is a general principle for decentralized clients to train a server
model collectively without sharing local data. FL is a promising framework with practical …