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，因为它使多个参与者能够在无需共享本地数据的情况下共同训练模型。目前，跨孤岛联邦学习是一种流行的训练设置，它利用数百个可靠的数据孤岛及高速访问链路来训练模型。尽管该方法已在现实场景中得到广泛应用，但设计鲁棒的拓扑结构以减少训练时间仍是一个开放性问题。本文提出了一种面向跨孤岛联邦学习的新颖多图拓扑。我们首先利用覆盖图构建多图，随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缩短了训练时间。我们的代码可在https://github.com/aioz-ai/MultigraphFL获取。