Chrome Extension
WeChat Mini Program
Use on ChatGLM

Secure and Efficient Federated Learning Via Novel Multi-Party Computation and Compressed Sensing

Lvjun Chen,Di Xiao, Zhuyang Yu,Maolan Zhang

INFORMATION SCIENCES(2024)

Chongqing Univ

Cited 0|Views14
Abstract
Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL- IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees.
More
Translated text
Key words
Federated learning,Compressed sensing,Secure multi-party computation,Functional encryption,Internet of things
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文设计了一套基于机器视觉的苹果智能在线检测分级系统,实现了苹果质量的自动、快速、准确分级,提高了自动化分级水平,分级总准确率达到95%。

方法】:采用阈值分割法对苹果正面图像进行分割,通过逐像素遍历法提取苹果外轮廓,计算各点至重心距离提取苹果大小特征,以及通过计算苹果横纵径比值提取果实形状特征。使用支持向量机(SVM)方法进行苹果两侧分离,通过计算红色像素占整个苹果像素的比例提取苹果颜色特征,使用Fisher统计法提取缺陷部分。

实验】:以寒富苹果为测试对象,使用该系统对400个苹果样本进行分级,实验结果显示系统总分级准确率为95%。