Federated Learning (FL) is a type of distributed deep learning framework in which multiple
devices train a local model using local data, and the gradients of the local model are then …

Federated learning (FL) has gained great traction in recent years. It can provide a privacy-
preserving mechanism to train machine learning models on hidden data. However, most of …

Federated learning enables participants to collaborate on model training without directly
exchanging raw data. Existing federated learning methods often follow the parameter server …

Federated learning (FL) enables collaborative training of machine learning models while
protecting the privacy of data. Traditional FL heavily relies on a trusted centralized server. It …

Federated Learning (FL) has emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

Blockchain and Deep Learning (DL) are two of the most revolutionary concepts in the field of
Computer Science. Both have made astounding leaps in research and application areas …

Federated Learning (FL) has been an emerging trend in machine learning and artificial
intelligence. It allows multiple participants to collaboratively train a better global model and …

Federated learning (FL), as a distributed machine learning paradigm, promotes personal
privacy by local data processing at each client. However, relying on a centralized server for …

Federated learning (FL), as a distributed machine learning approach, has drawn a great
amount of attention in recent years. FL shows an inherent advantage in privacy preservation …

Federated Learning (FL) is a decentralized machine learning protocol that allows a set of
participating agents to collaboratively train a model without sharing their data. This makes …