Federated learning, an innovative methodology that enables clients to train a global model
collectively without disclosing raw data, protects data privacy when it comes to training …

Federated learning has emerged as a promising approach for collaborative and privacy-
preserving learning. Participants in a federated learning process cooperatively train a model …

Recently, Federated Learning (FL) has received significant attention in collaborative and
privacy preserving model training across different application areas. FL is a part of machine …

Federated learning enables multiple data owners to jointly train a machine learning model
without revealing their private datasets. However, a malicious aggregation server might use …

Federated learning is an improved version of distributed machine learning that further
offloads operations which would usually be performed by a central server. The server …

Most work in privacy-preserving federated learning (FL) has been focusing on horizontally
partitioned datasets where clients share the same sets of features and can train complete …

While Federated Learning (FL) enables training by only sharing model updates rather than
data, FL can still be prone to privacy leaks. Therefore, many efforts have been made to adopt …

The majority of work in privacy-preserving federated learning (FL) has been focusing on
horizontally partitioned datasets where clients share the same sets of features and can train …

Although supporting training deep learning models distributed without disclosing the raw
privacy data, federated learning (FL) is still vulnerable to inference attacks. This paper …

In recent years, federated learning has become a new way to solve the problems of data
island and privacy leakage in machine learning. Federated learning architecture does not …