Privacy-preserving Federated Adversarial Domain Adaptation over Feature Groups for Interpretability

Kang, Yan; He, Yuanqin; Luo, Jiahuan; Fan, Tao; Liu, Yang; Yang, Qiang

doi:10.1109/tbdata.2022.3188292

Cited by 9 publications

(2 citation statements)

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“…In this setting, [73,225] design PSFT algorithms about linear regression and xgboost based on SMPC. [87,46,38,65,102,233,49,254] consider the design of the PSFT algorithms by using HE. Specifically, [87,233] propose PSFT algorithms on logistic regression.…”

Section: Smpc-based Psftmentioning

confidence: 99%

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A survey on security and privacy threats to federated learning

Zhang

Zeng

et al. 2021

2021 International Conference on Networking and Network Applications (NaNA)

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Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.

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Section: Smpc-based Psftmentioning

confidence: 99%

“…[46,38,65] propose the PSFT algorithm about tree-based model. [102,49,254] design PSFT algorithms about neural network. Furthermore, Chamani and Papadopoulos [29] uses TEE to design PSFT algorithm.…”

Section: Smpc-based Psftmentioning

confidence: 99%