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

arXiv: https://arxiv.org/abs/2605.06433 · HTML: https://mlsanity.com/paper/2605.06433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [Identity-aware Graph Neural Networks](https://mlsanity.com/paper/2101.10320/) (2021)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)
- [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2102.04925/) (2021)
- [Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNs](https://mlsanity.com/paper/2210.13978/) (2022)
- [Realistic Synthetic Financial Transactions for Anti-Money Laundering Models](https://mlsanity.com/paper/2306.16424/) (2023)
- [Graph Positional Encoding via Random Feature Propagation](https://mlsanity.com/paper/2303.02918/) (2023)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](https://mlsanity.com/paper/2201.12433/) (2022)
- [Fragment-based Pretraining and Finetuning on Molecular Graphs](https://mlsanity.com/paper/2310.03274/) (2023)
- [MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning](https://mlsanity.com/paper/2310.09831/) (2023)
- [Deep Efficient Private Neighbor Generation for Subgraph Federated Learning](https://mlsanity.com/paper/2401.04336/) (2024)
- [Federated Learning over Coupled Graphs](https://mlsanity.com/paper/2301.11099/) (2023)
- [Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks](https://mlsanity.com/paper/2210.11020/) (2022)
- [Decoupled Subgraph Federated Learning](https://mlsanity.com/paper/2402.19163/) (2024)
- [Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval](https://mlsanity.com/paper/2510.22538/) (2025)
- [Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks](https://mlsanity.com/paper/2507.20226/) (2025)

## Abstract (arXiv)

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.