论文标题
联合学习的私有图
Privatized Graph Federated Learning
论文作者
论文摘要
Federated Learning是一种半分布的算法，服务器与多个分散客户端进行通信以学习全局模型。联合体系结构并不强大，并且由于其一级多客户结构而对通信和计算过载敏感。它也可能受到针对交流链接的个人信息的隐私攻击。在这项工作中，我们介绍了联合学习图（GFL），该图由多个由图形连接的联合单元组成。然后，我们展示如何使用图形同构扰动来确保算法在差异上是私有的。我们进行融合和隐私理论分析，并通过计算机模拟说明性能。
Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting personal information on the communication links. In this work, we introduce graph federated learning (GFL), which consists of multiple federated units connected by a graph. We then show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private. We conduct both convergence and privacy theoretical analyses and illustrate performance by means of computer simulations.
