A Comprehensive Survey of Privacy-preserving Federated Learning

By Xuefei Yin, Yanming Zhu, Jiankun Hu. ACM Computing Surveys, 2021

667 citations recorded by the source corpus.

The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/bced3380c6524e150fa478027d751786637a2fe7

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. Bo Du
2. Mang Ye
3. Lichao Sun
4. Pong Chi Yuen
5. Dacheng Tao
6. Philip S. Yu
7. Yong Xiang
8. Zhiyi Tian

Citations in the corpus

• Federated learning for medical image analysis: A survey (2023, background): “There have been several survey papers on federated learning [15]–[20], but further technical details about facilitating federated learning in medicine and healthcare are not yet covered.”
• Asynchronous Federated Learning on Heterogeneous Devices: A Survey (2021, background): “The design goal of FL is to ensure personal data privacy and develop satisfying ML models across multiple participants or computing nodes under the premise of legal compliance [50, 115].”
• A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, background): “, machine learning [93] and decentralized learning [7, 148].”
• Membership Inference Attacks on Machine Learning: A Survey (2021, background): “There are many surveys that summarize different attacks on ML models [35, 84, 114, 117, 137, 155, 166, 168, 176, 190, 228].”
• Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, methodology): “These methods generally fall into three categories [232], namely data encryption, perturbation and anonymization (Fig.”