此外,云服务器可以试图构建一个假的proof或返回一个错误的聚合结果来诱导用户接受这个信息。 方案技术细节:VerifyNet的目的是解决联邦训练过程中存在的三个问题。一是保护工作流中用户局部梯度的隐私。其次,为了防止服务器的恶意欺骗,我们的VerifyNet支持每个用户有效地验证服务器返回结果的正确性。第三,在培训过程中,Veri...
【论文阅读笔记】 VerifyNet: Secure and Verifiable Federated Learning 摘要: 本文主要针对于两个问题: 1)如何保护用户上传到云服务器的信息(eg.梯度信息) 2)如何保证服务器正确的聚合了用户上传的信息。提出了一种可验证的联邦学习方案,第一次将可验证引入到联邦学习中,第一次提出了双盲化的方法保护了用户上传的...
摘要: As an emerging training model with neural networks, federated learning has received widespread attention due to its ability to update parameters without collecting users' raw data. However, ...关键词: Privacy-preserving deep learning verifiable federated learning cloud computing ...
-Verifiable Federated Learning [Submission Instructions] We invite submissions of original research papers, case studies, and position papers related to the workshop's themes. Submissions should follow the latest ACM Sigconf style conference format (https://www.acm.org/publications/proceedings-template) ...
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preser...
A Verifiable Federated Learning Scheme Based on Secure Multi-party ComputationFederated learning ensures that the quality of the model is uncompromised while the resulting global model is consistent with the model trained by directly collecting user data. However, the risk of inferring data considered ...
Machine learning 1. Introduction In the past few years,exponential growthhas been observed in medical data generation through smart healthcare systems[1]. Most of the data are gathered using theInternet of Things(IoT). The IoT assimilated into various medical devices and equipment, led to the ev...
As a promising paradigm of distributed learning, federated learning has garnered considerable attention since its emergence. However, traditional federated learning solutions based on a central server are not efficient and scalable. Moreover, the centralized design relies on a trustworthy party coordinating...
The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethic
randomandinformed. To ensure random selection without a trusted server, Lotto enables each client to autonomously determine their participation usingverifiable randomness. For informed selection, which is more vulnerable to manipulation, Lotto approximates the algorithm by employing random selection within a...