Federated learning (FL) has recently attracted much attention due to its advantages for data
privacy. But every coin has two sides: protecting users' data (not requiring users to send their …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-
preserving machine learning for mobile clients. While traditional FL has demonstrated its …

Federated learning (FL) is a distributed machine learning paradigm that enables scattered
clients to collaboratively train a shared global model. FL is suitable for privacy-preserving …

Federated Learning (FL), as a new privacy learning paradigm, can complete the learning
task without compromising user privacy. With the enhancement of user data protection …

Distributed intelligence enables the widespread deployment of AI technology, greatly
promoting the development of AI. Federated learning is a widely used distributed …

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning
(ML) in a distributed manner without directly accessing private data on clients. How-ever, FL …

Federated learning came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

Data security and user privacy-issue have become an important field. As federated learning
(FL) could solve the problems from data security and privacy-issue, it starts to be applied in …

Federated learning (FL), an emerging machine learning paradigm that trains a global model
across distributed clients without violating data privacy, has recently attracted significant …