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A survey of federated learning for edge computing: Research problems and solutions - 2021

A Survey Of Federated Learning For Edge Computing: Research Problems And Solutions

Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Qi Xia, Winson Ye, Zeyi Tao,Jindi Wu, Qun Li

Journal name:  High-Confidence Computing

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  https://doi.org/10.1016/j.hcc.2021.100008

Volume Information:  Volume 1, Issue 1, June 2021, 100008