Title: FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting Show Chinese title

Title: FedSA-GCL：一种具有个性化聚合和聚类感知广播的半异步联邦图学习框架

Abstract: Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple local systems. However, most existing FGL approaches rely on synchronous communication, which leads to inefficiencies and is often impractical in real-world deployments. Meanwhile, current asynchronous federated learning (AFL) methods are primarily designed for conventional tasks such as image classification and natural language processing, without accounting for the unique topological properties of graph data. Directly applying these methods to graph learning can possibly result in semantic drift and representational inconsistency in the global model. To address these challenges, we propose FedSA-GCL, a semi-asynchronous federated framework that leverages both inter-client label distribution divergence and graph topological characteristics through a novel ClusterCast mechanism for efficient training. We evaluate FedSA-GCL on multiple real-world graph datasets using the Louvain and Metis split algorithms, and compare it against 9 baselines. Extensive experiments demonstrate that our method achieves strong robustness and outstanding efficiency, outperforming the baselines by an average of 2.92% with the Louvain and by 3.4% with the Metis. Show Chinese abstract

Abstract: 联邦图学习（FGL）是一种分布式学习范式，允许在多个本地系统上的大规模子图上进行协作训练。 然而，大多数现有的FGL方法依赖于同步通信，这会导致效率低下，并且在实际部署中往往不切实际。 同时，当前的异步联邦学习（AFL）方法主要针对图像分类和自然语言处理等传统任务设计，而未考虑图数据的独特拓扑特性。 直接将这些方法应用于图学习可能导致全局模型中的语义漂移和表示不一致。 为了解决这些挑战，我们提出了FedSA-GCL，这是一种半异步联邦框架，通过一种新颖的ClusterCast机制，利用客户端间标签分布差异和图拓扑特性以实现高效的训练。 我们在多个真实世界的图数据集上使用Louvain和Metis分割算法评估FedSA-GCL，并与9个基线方法进行比较。 大量实验表明，我们的方法表现出强大的鲁棒性和卓越的效率，在Louvain下平均优于基线2.92%，在Metis下平均优于基线3.4%。