Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …

Privacy-preserving federated learning, as one of the privacy-preserving computation
techniques, is a promising distributed and privacy-preserving machine learning (ML) …

Edge intelligent computing is widely used in the fields, such as the Internet of Medical
Things (IoMT), which has advantages, including high data processing efficiency, strong real …

Over the past years, the increasingly severe data island problem has spawned an emerging
distributed deep learning framework—federated learning, in which the global model can be …

Federated learning (FL) is a collaborative, decentralized privacy‐preserving method to
attach the challenges of storing data and data privacy. Artificial intelligence, machine …

Privacy-preserving federated learning is distributed machine learning where multiple
collaborators train a model through protected gradients. To achieve robustness to users …

With the advance of machine learning technology and especially the explosive growth of big
data, federated learning, which allows multiple participants to jointly train a high-quality …

Federated Learning (FL) is a setting where multiple parties with distributed data collaborate
in training a joint Machine Learning (ML) model while keeping all data local at the parties …

Healthcare data explosion and cloud computing booming have motivated healthcare
centers to outsource their healthcare data and data-driven services to a powerful cloud …

In this article, we elaborate on a design and realization of fuzzy rule-based model in the
horizontal federated learning framework. Traditional machine learning in distributed …