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

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 be-cause 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 dis-persed 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.