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 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) 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 …

Motivated by the increasingly powerful computing capabilities of end-user equipment, and
by the growing privacy concerns over sharing sensitive raw data, a distributed machine …

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 …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

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 …

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 promising paradigm to realize distributed machine learning on
heterogeneous clients without exposing their private data. However, there is the risk of …