论文标题
通过分层联合学习增强隐私
Enhancing Privacy via Hierarchical Federated Learning
论文作者
论文摘要
联邦学习遇到了几个与隐私有关的问题，这些问题使参与者面临各种威胁。联邦学习的集中式体系结构加剧了许多这些问题。在本文中，我们讨论将联合学习应用于分层体系结构作为潜在解决方案。我们介绍了对培训过程及其对参与者隐私的影响更灵活地分散控制的机会。此外，我们研究了提高国防和验证方法的效率和有效性的可能性。
Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federated learning on a hierarchical architecture as a potential solution. We introduce the opportunities for more flexible decentralized control over the training process and its impact on the participants' privacy. Furthermore, we investigate possibilities to enhance the efficiency and effectiveness of defense and verification methods.
