论文标题
用图形的个性化联合学习
Personalized Federated Learning With Graph
论文作者
论文摘要
知识共享和模型个性化是个性化联合学习（PFL）概念框架中的两个关键组成部分。现有的PFL方法着重于提出新的模型个性化机制，同时简单地通过从所有客户端汇总模型来实现知识共享，而不论其关系图如何。本文旨在通过利用客户之间的基于图的结构信息来增强PFL的知识共享过程。我们提出了一个新颖的结构化联合学习（SFL）框架，以同时使用客户端关系图和客户端的私人数据同时学习全球和个性化模型。我们将SFL与Graph一起投入到一个新颖的优化问题中，该问题可以通过统一的框架对客户的复杂关系和基于图的结构拓扑进行建模。此外，除了使用现有关系图外，SFL还可以扩展以了解客户之间的隐藏关系。有关流量和图像基准数据集的实验可以证明该方法的有效性。所有实施代码均可在GitHub上找到
Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph. This paper aims to enhance the knowledge-sharing process in PFL by leveraging the graph-based structural information among clients. We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data. We cast SFL with graph into a novel optimization problem that can model the client-wise complex relations and graph-based structural topology by a unified framework. Moreover, in addition to using an existing relation graph, SFL could be expanded to learn the hidden relations among clients. Experiments on traffic and image benchmark datasets can demonstrate the effectiveness of the proposed method. All implementation codes are available on Github
