A survey on federated learning: challenges and applications

Abstract

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL.

Keywords: Federated learning; Machine learning; Personalized federated learning; Privacy protection.

Fig. 1

The basic framework of FL

Fig. 2

The categories of FL

Fig. 3

Heterogeneity challenge and solutions in a federated learning environment

Fig. 4

Federated intelligent medicine

Fig. 5

Federated recommendation system

Fig. 6

Federated learning in smart city

Fig. 7

Federated learning applications in financial

Fig. 8

Federated learning in edge computing

Fig. 9

Federated intrusion detection