Abstract:

[Objective] Federated learning is a current research hotspot. In this paper, we review the research and progress of federated learning methods in recent years from the perspective of the model architecture. [Coverage] This paper uses keyword search and citation secondary search to collect papers from international journals and conferences on computer. [Methods] Based on a brief discussion of the definition, architecture, and three heterogeneity problems of federated learning, the federated learning algorithms are divided into two categories, learning global models and learning personalized models. We further discuss the federated learning methods in the two categories including the datasets used, the solution to heterogeneous problems, and the advantages and disadvantages of each method. [Results] Existing federated learning methods can both learn global models of strong generalization performance and personalized local models. Researchers are more concerned with data heterogeneous problems than device heterogeneous problems, and the datasets used in testing are usually conventional machine learning datasets. [Conclusions] The field of federated learning is developing rapidly, but there are still some problems of insufficient research on heterogeneous problems and immature benchmarking. It is believed that there will be more solutions to the problem of federation heterogeneity in real scenarios in the future.

Key words: federated learning, personalized model, global model, heterogeneity problem