Survey on Attacks and Defenses in Federated Learning

Abstract

Federated learning is proposed to solve the contradiction between data sharing and privacy-preserving.It aims to build collaborative models by securely interacting irreversible information (e.g.,model parameters or gradient updates).However,the risks of privacy leakage and malicious attacks in the process of model local training,information interaction and parameter transmission have brought major challenges to the practical application of federated learning.This paper summarizes the Attack beha-viors and corresponding defense strategies in the modeling and deployment process of federated learning.Firstly,this paper briefly reviews the development process of federated learning and the basic modeling process.Next,it classifies attack behaviors in fede-ral learning training and deployment from three aspects:confidentiality,availability and integrity,and combs the latest research on privacy theft and malicious attacks.Then,it summarizes defense countermeasures from two directions: honest-but-curious attac-kers and malicious attackers,and analyzes the defense capabilities of different strategies.Finally,it presents some discussions about the problems and challenges of attack and defense methods in the practice of federated learning.Besides,it looks forward to their future development direction of federated learning in defense strategy and system design.

Keywords

• federated learning|data security|attacks and defenses|data fusion|machine learning