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 …

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 …

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 …

Federated Learning (FL) has emerged as a privacy-preserving distributed Machine Learning
paradigm, which collaboratively trains a shared global model across a number of end …

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

Federated learning has been widely studied and applied to various scenarios, such as
financial credit, medical identification, and so on. Under these settings, federated learning …

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