Privacy and Robustness in Federated Learning: Attacks and Defenses

By Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu. IEEE Transactions on Neural Networks and Learning Systems, 2020

538 citations recorded by the source corpus.

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continues to thrive in this new reality. Existing FL protocol designs have been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this article, we conduct a comprehensive survey on privacy and robustness in FL over the past five years. Through a concise introduction to the concept of FL and a unique taxonomy covering: 1) threat models; 2) privacy attacks and defenses; and 3) poisoning attacks and defenses, we provide an accessible review of this important topic. We highlight the intuitions, key techniques, and fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions toward robust and privacy-preserving FL, and their interplays with the multidisciplinary goals of FL.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8448010d9adad18bf36070c012770a10ecb21c76

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mingxia Liu
2. Dacheng Tao
3. Bo Du
4. Qiang Yang
5. Mang Ye
6. Pong Chi Yuen
7. Han Yu
8. Xiuwen Fang

Citations in the corpus

• Federated Learning for Malware Detection in IoT Devices (2021, background): “These attacks have been selected for their simplicity and variety, but other more sophisticated and stealthy attacks exist in the literature [16, 41].”
• Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “Currently, many researches [12, 143, 248] dedicate to addressing privacy issues in federated learning.”
• Towards Personalized Federated Learning (2021, background): “Although FL offers better privacy protection compared to traditional centralized model training approaches, recent research has exposed vulnerabilities of FL that could potentially compromise data privacy [16].”
• Federated learning for medical image analysis: A survey (2023, background): “Several existing FL systems have been shown to be vulnerable to inside or outside attacks, concerning system robustness and data privacy (Lyu et al., 2022).”
• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges (2022, background): “These surveys lack of an holistic view of FL and the review of the defences against adversarial attacks, because of the following reasons: (1) most of them are only focused on one kind of adversarial attacks, namely there are surveys reviewing attacks to the federated model [18, 19, 20] or privacy a”