跨客户端图模式检测，通过分层交换中间表示解决分布式学习中的表征差异问题。
Federated Cross-Client Subgraph Pattern Detection
- 各客户端在前向传播每层时交换中间节点表示，不暴露原始数据。
- 实验表明，每步更新交换的嵌入比每轮更新更有效，可恢复大部分表征差距。
- 适合需要隐私保护的多源图数据联合分析场景，如医疗或金融网络建模。
子图模式检测旨在发现图中复杂的交互结构。然而，现有基于图神经网络（GNN）的方法依赖于对全图的集中式访问。当图数据分布在多个参与方时，客户端本地的GNN计算会偏离集中式模型，导致表征等价性差距。我们将此问题形式化为结构可观测性问题：跨越划分边界的子图模式在本地变得无法识别。为此，我们提出一种逐步、分层的嵌入交换框架，客户端在前向传播每一层时同步中间节点表示，而不暴露原始特征或标签。在扩展子图假设和客户端共享模型参数的前提下，该框架能恢复与集中式GNN在全图上的相同节点表示。在含环、双团和散射-聚集模式的合成有向多重图上进行实验，结果表明嵌入交换与联邦参数聚合具有互补性而非互换性：两者的结合能有效恢复表征差距，前提是交换的嵌入为每步更新而非每轮更新。
原文摘要 · Abstract (English)
Subgraph pattern detection aims to uncover complex interaction structures in graphs. However, state-of-the-art graph neural network (GNN)-based solutions assume centralized access to the entire graph. When graphs are instead distributed across multiple parties, client-local GNN computations diverge from those of a centralized model, resulting in a representation-equivalence gap. We formalize this as a structural observability problem, where subgraph patterns crossing partition boundaries become locally unidentifiable. To bridge this gap, we propose a per-step, layer-wise embedding exchange framework in which clients synchronize intermediate node representations at each layer of the forward pass, without exposing raw features or labels. Under an extended-subgraph assumption and shared model parameters across clients, this framework recovers the same node representations as a centralized GNN over the full graph. Experiments on synthetic directed multigraphs with cycles, bicliques, and scatter-gather patterns show that embedding exchange and federated parameter aggregation are complementary rather than interchangeable: their combination recovers most of the representation gap, provided exchanged embeddings are fresh per-step rather than stale per-epoch.
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