Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning is a popular training setting that utilizes a few hundred reliable data silos with high-speed access links to training a model. While this approach has been widely applied in real-world scenarios, designing a robust topology to reduce the training time remains an open problem. In this paper, we present a new multigraph topology for cross-silo federated learning. We first construct the multigraph using the overlay graph. We then parse this multigraph into different simple graphs with isolated nodes. The existence of isolated nodes allows us to perform model aggregation without waiting for other nodes, hence effectively reducing the training time. Intensive experiments on three public datasets show that our proposed method significantly reduces the training time compared with recent state-of-the-art topologies while maintaining the accuracy of the learned model. Our code can be found at https://github.com/aioz-ai/MultigraphFL

翻译：联邦学习是一个活跃的研究课题，因为它能使多个参与方在无需共享本地数据的情况下共同训练模型。当前，跨筒仓联邦学习是一种流行的训练设置，它利用数百个具有高速接入链路的可靠数据筒仓来训练模型。虽然这种方法已广泛应用于实际场景，但设计一种能够减少训练时间的鲁棒拓扑结构仍是一个未解决的问题。在本文中，我们提出了一种用于跨筒仓联邦学习的新型多图拓扑。我们首先使用覆盖图构建多图，然后将该多图解析为包含孤立节点的不同简单图。孤立节点的存在使我们能够在不等待其他节点的情况下执行模型聚合，从而有效减少训练时间。在三个公开数据集上的密集实验表明，与近期最先进的拓扑结构相比，我们提出的方法在保持学习模型准确性的同时显著减少了训练时间。我们的代码可在 https://github.com/aioz-ai/MultigraphFL 获取。