Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detection

Subgraph pattern detection is a fundamental graph mining task central to anti-money laundering (AML) in finance. However, state-of-the-art graph neural network (GNN)-based methods assume centralized graph access, impractical when transaction data cannot be shared across institutions. In practice, graphs are distributed across clients, where local GNN training diverges from centralized learning, creating representation and adjoint equivalence gaps that induce a parameter-gradient gap. We formalize this as a structural observability problem, where AML patterns crossing subgraph boundaries become locally unidentifiable. To bridge these gaps, we propose a layer-wise exchange framework: clients synchronize node representations each forward pass layer and adjoints each backward pass, without exposing raw features or labels. Under a shared-parameter assumption, we prove node representations and parameter gradients are equivalent to those of centralized GNN training. Experiments on directed multigraphs with money-laundering patterns show layer-wise exchange performs best with per-step synchronization and remains effective under practical per-epoch synchronization. Our results show layer-wise exchange and parameter synchronization to be complementary, supporting distributed pattern detection in privacy-constrained financial settings.