论文标题
确保花的聚合在花中进行联合学习
Secure Aggregation for Federated Learning in Flower
论文作者
论文摘要
联合学习（FL）允许当事方通过将培训计算委派给客户并汇总服务器上的所有单独培训模型来学习共享的预测模型。为了防止从本地模型推断出的私人信息，使用安全的聚合（SA）协议用于确保服务器在汇总时无法检查其训练有素的模型。但是，在FL框架中，SA的当前实现存在局限性，包括易受客户端辍学或配置困难的脆弱性。 在本文中，我们介绍了Salvia，这是FL FL框架中为Python用户实施的SA。基于半honest威胁模型的SECAGG（+）协议，Salvia对客户辍学非常强大，并揭示了与各种机器学习框架兼容的灵活且易于使用的API。我们表明，萨尔维亚的实验性能与Secagg（+）的理论计算和通信复杂性一致。
Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local models, Secure Aggregation (SA) protocols are used to ensure that the server is unable to inspect individual trained models as it aggregates them. However, current implementations of SA in FL frameworks have limitations, including vulnerability to client dropouts or configuration difficulties. In this paper, we present Salvia, an implementation of SA for Python users in the Flower FL framework. Based on the SecAgg(+) protocols for a semi-honest threat model, Salvia is robust against client dropouts and exposes a flexible and easy-to-use API that is compatible with various machine learning frameworks. We show that Salvia's experimental performance is consistent with SecAgg(+)'s theoretical computation and communication complexities.
