Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Federated learning has received a great deal of research attention recently, with privacy
protection becoming a key factor in the development of artificial intelligence. Federated …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

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

Recently, federated learning (FL) has emerged as a promising distributed machine learning
(ML) technology, owing to the advancing computational and sensing capacities of end-user …

In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Federated learning faces many security and privacy issues. Among them, poisoning attacks
can significantly impact global models, and malicious attackers can prevent global models …

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

Federated learning (FL) enables participating parties to collaboratively build a global model
with boosted utility without disclosing private data information. Appropriate protection …