标题： TAPFed：隐私保护联邦学习的阈值安全聚合 显示英文标题

标题： TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

摘要： 联邦学习是一种计算范式，通过使多个参与方协作训练机器学习模型而不泄露个人数据来增强隐私。 然而，当前的研究表明，传统的联邦学习平台由于梯度交换导致的隐私泄露，无法确保隐私。 为了实现隐私保护的联邦学习，集成安全聚合机制是必不可少的。 不幸的是，现有解决方案容易受到最近展示的推理攻击，如解聚攻击。 本文提出了 TAPFed，一种在存在恶意参与者的情况下实现隐私保护联邦学习的方法，适用于多个去中心化聚合器。 TAPFed 使用了一种提出的阈值功能加密方案，并允许一定数量的恶意聚合器，同时保持安全性和隐私性。 我们提供了 TAPFed 的正式安全性和隐私性分析，并通过实验评估将其与各种基线进行比较。 我们的结果表明，TAPFed 在模型质量方面与最先进的方法相当，同时在不同的模型训练场景中将传输开销减少了 29%-45%。 最重要的是，TAPFed 可以抵御由好奇聚合器引起的最近展示的推理攻击，而大多数现有方法容易受到此类攻击。 显示英文摘要

摘要： Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by the interchange of gradients. To achieve privacy-preserving federated learning, integrating secure aggregation mechanisms is essential. Unfortunately, existing solutions are vulnerable to recently demonstrated inference attacks such as the disaggregation attack. This paper proposes TAPFed, an approach for achieving privacy-preserving federated learning in the context of multiple decentralized aggregators with malicious actors. TAPFed uses a proposed threshold functional encryption scheme and allows for a certain number of malicious aggregators while maintaining security and privacy. We provide formal security and privacy analyses of TAPFed and compare it to various baselines through experimental evaluation. Our results show that TAPFed offers equivalent performance in terms of model quality compared to state-of-the-art approaches while reducing transmission overhead by 29%-45% across different model training scenarios. Most importantly, TAPFed can defend against recently demonstrated inference attacks caused by curious aggregators, which the majority of existing approaches are susceptible to.