Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

With the technological advances in machine learning, effective ways are available to
process the huge amount of data generated in real life. However, issues of privacy and …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

As a novel distributed learning mechanism, federated learning has drawn widespread
attention by allowing multiple parties to train an accurate model collaboratively without …

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 machine learning (FL) allows to collectively train models on sensitive data as only
the clients' models and not their training data need to be shared. However, despite the …

Federal learning (FL) can realize a distributed training machine learning models in multiple
devices while protecting their data privacy, but some defect still exists such as single point …