Research Area:  Machine Learning
Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. It can provide better data privacy because training data are not transmitted to a central server. Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dispersed edge devices. To build such an edge federated learning system, we need to tackle a number of technical challenges. In this survey, we provide a new perspective on the applications, development tools, communication efficiency, security & privacy, migration and scheduling in edge federated learning.
Author(s) Name:  Qi Xia, Winson Ye, Zeyi Tao,Jindi Wu, Qun Li
Journal name:  High-Confidence Computing
Publisher name:  ELSEVIER
Volume Information:  Volume 1, Issue 1, June 2021, 100008
Paper Link:   https://www.sciencedirect.com/science/article/pii/S266729522100009X