With the expansion of the Internet of Things (IoT) development and application, federated
learning has gained higher popularity in industrial researching fields. However, the security …

Federated learning (FL) is a machine learning technique that allows multiple devices to train
a model collaboratively without sharing their data with a central server. It has advantages …

The integration of blockchain-based federated learning (BFL) and Industry 4.0 utilizes
intermediate models to execute task deployment and result acceptance, effectively solving …

As an emerging distributed machine learning (ML) method, federated learning (FL) can
protect data privacy through collaborative learning of artificial intelligence (AI) models across …

Federated Learning (FL) provides privacy preservation by allowing the model training at
edge devices without the need of sending the data from edge to a centralized server. FL has …

Federated learning enables participants to be capable of collaboratively building powerful
machine learning models and exploiting privacy protection mechanisms to protect data …

Federated learning is a privacy-preserving machine learning framework where multiple data
owners collaborate to train a global model under the orchestra of a central server. The local …

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead
of sharing raw data, the federated learning process cooperatively exchanges the model …

Federated learning (FL) is a new solution to fulfill machine learning (ML) in a decentralized
manner. In FL, a group of participants train their local models using their private data, and …

Federated learning (FL) is a promising way to allow multiple data owners (clients) to
collaboratively train machine learning models without compromising data privacy. Yet …