Federated learning (FL) has been used as a promising approach to breaking the dilemma
between the data privacy and the learning from large collections of distributed data. Without …

Federated Learning (FL) is currently a widely used collaborative learning framework, and
the distinguished feature of FL is that the clients involved in training do not need to share …

Recent studies have revealed that federated learning (FL), once considered secure due to
clients not sharing their private data with the server, is vulnerable to attacks such as client …

Research on federated learning has continued to develop over the past few years. Many
federated learning algorithms and frameworks have been developed to ensure model …

Federated learning aims to complete model training without private data sharing, but many
privacy risks remain. Recent studies have shown that federated learning is vulnerable to …

Existing federated learning (FL) designs have been shown to exhibit vulnerabilities which
can be exploited by adversaries to compromise data privacy. However, most current works …

Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to
collaborate on building a machine learning model without sharing their private data …

Federated Learning (FL) is a machine learning technique that allows multiple data owners to
collaborate in training a model without sharing their training data. However, FL systems are …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

Federated Learning (FL) is a distributed machine learning technique where multiple devices
(such as smartphones or IoT devices) train a shared global model by using their local data …