The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

In the past decades, artificial intelligence (AI) has achieved unprecedented success, where
statistical models become the central entity in AI. However, the centralized training and …

Federated learning allows distributed users to collaboratively train a model while keeping
each user's data private. Recently, a growing body of work has demonstrated that an …

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) has received a great deal of research attention in the context of
privacy protection restrictions. By jointly training deep learning models, a variety of training …

The trend of the next generation of the internet has already been scrutinized by top analytics
enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the …

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Federated learning enables multiple users to build a joint model by sharing their model
updates (gradients), while their raw data remains local on their devices. In contrast to the …

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

Federated Machine Learning has recently become a prominent approach to leverage data
that is distributed across different clients, without the need to centralize data. Models are …