为去中心化图学习设计自适应通信拓扑，提升模型聚合效率。
DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
- 基于客户端局部子图结构与语义信息动态构建通信拓扑
- 在8个真实数据集上平均准确率提升3.26%
- 适合处理异构图数据的去中心化学习场景
去中心化联邦学习（DFL）作为一种分布式范式，避免了中心化架构的单点故障和通信瓶颈问题。然而，现有DFL优化策略主要针对计算机视觉任务设计，未能有效利用客户端局部子图固有的拓扑信息。尽管联邦图学习（FGL）专为图数据设计，但大多采用中心化服务器-客户端模型，未发挥去中心化优势。为此，我们提出DFed-SST，一种具备自适应通信的去中心化联邦图学习框架。其核心是双拓扑自适应通信机制，利用各客户端局部子图的独特拓扑特征，动态构建并优化客户端间通信拓扑，从而在面对数据异构性时高效引导模型聚合。在8个真实世界数据集上的大量实验一致表明，DFed-SST相比基线方法平均准确率提升3.26%。
原文摘要 · Abstract (English)
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predominantly implemented in a centralized server-client model, failing to leverage the benefits of decentralization.To bridge this gap, we propose DFed-SST, a decentralized federated graph learning framework with adaptive communication. The core of our method is a dual-topology adaptive communication mechanism that leverages the unique topological features of each client's local subgraph to dynamically construct and optimize the inter-client communication topology. This allows our framework to guide model aggregation efficiently in the face of heterogeneity. Extensive experiments on eight real-world datasets consistently demonstrate the superiority of DFed-SST, achieving 3.26% improvement in average accuracy over baseline methods.
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