The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …

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 …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

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 …

Federated learning (FL) facilitates distributed training across different IoT and edge devices,
safeguarding the privacy of their data. The inherent distributed structure of FL introduces …

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 …

Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …