  As an emerging technique, Federated Learning (FL) can jointly train a global
model with the data remaining locally, which effectively solves the problem of
data privacy protection through the encryption mechanism. The clients train
their local model, and the server aggregates models until convergence. In this
process, the server uses an incentive mechanism to encourage clients to
contribute high-quality and large-volume data to improve the global model.
Although some works have applied FL to the Internet of Things (IoT), medicine,
manufacturing, etc., the application of FL is still in its infancy, and many
related issues need to be solved. Improving the quality of FL models is one of
the current research hotspots and challenging tasks. This paper systematically
reviews and objectively analyzes the approaches to improving the quality of FL
models. We are also interested in the research and application trends of FL and
the effect comparison between FL and non-FL because the practitioners usually
worry that achieving privacy protection needs compromising learning quality. We
use a systematic review method to analyze 147 latest articles related to FL.
This review provides useful information and insights to both academia and
practitioners from the industry. We investigate research questions about
academic research and industrial application trends of FL, essential factors
affecting the quality of FL models, and compare FL and non-FL algorithms in
terms of learning quality. Based on our review's conclusion, we give some
suggestions for improving the FL model quality. Finally, we propose an FL
application framework for practitioners.
