Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

Privacy preservation is a critical concern in Federated Learning (FL). However, traditional
Local Differential Privacy (LDP) methods face challenges in balancing FL model accuracy …

In an era where early detection of diseases is paramount, integrating artificial intelligence
(AI) into routine lung cancer screening offers a groundbreaking approach to simultaneously …

The computational cost for implementing data privacy protection tends to rise as the
dimensions increase, especially on correlated datasets. For this reason, a faster data …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

This chapter explores the growing field of secure multi-party computation (SMPC), an
important part of modern cryptography that protects privacy in group computing tasks. It does …

The emergence of artificial intelligence (AI) in drug discovery represents a transformative
development in addressing neglected diseases, particularly in the context of the developing …

The ever-evolving domain of machine learning has witnessed significant advancements with
the advent of federated learning, a paradigm revered for its capacity to facilitate model …

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive
volume of data creates significant challenges for traditional cloud-based solutions. These …

The problem of data privacy protection in the information age deserves people's attention.
As a distributed machine learning technology, federated learning can effectively solve the …