Group verifiable secure aggregate federated learning based on secret sharing.

IF 3.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Reports Pub Date : 2025-03-21 DOI:10.1038/s41598-025-94478-0
Sufang Zhou, Lin Wang, Liangyi Chen, Yifeng Wang, Ke Yuan
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Abstract

Federated learning is a distributed machine learning approach designed to tackle the problems of data silos and the security of raw data. Nevertheless, it remains susceptible to privacy leakage risks and aggregation server tampering attacks. Current privacy-preserving methods often involve significant computational and communication overheads, which can be challenging in resource-limited settings, hindering their practical application. To overcome these obstacles, this article proposes an efficient secure aggregation scheme based on secret sharing-GVSA. GVSA safeguards the privacy of local models through a masking technique and improves the system's resilience to user dropouts by utilizing secret sharing. Furthermore, GVSA implements a dual aggregation approach and incorporates lightweight validation tags to verify the accuracy of the aggregation results. By adopting a grouping strategy, GVSA effectively minimizes the computational burden on both users and the server, making it well-suited for resource-constrained environments. We compare GVSA with leading existing methods and assess its performance through various experimental setups. Experimental results demonstrate that GVSA maintains high security while effectively preserving model accuracy. Compared to FedAvg, GVSA incurs only approximately 7% additional computational overhead. Furthermore, compared to other secure aggregation schemes with the same security level, GVSA achieves approximately a 2.3× improvement in training speed.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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