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Open Source · Apache 2.0 · Federated Graph Protocol

Federated Typology & Signal Registry

The open-source registry for federated graph typologies and detection signals. Version, distribute, and validate production-grade models across distributed institutional nodes.

Get started free Browse signals & models

15

Typologies

8

Models

1,054

Pulls

7

Nodes

Browse Signals

Fraud typologies and detection patterns across the federated network.

15 typologies
Signal Slug Upvotes Pulls Submissions

Crypto Mixing / Tumbling

Obfuscating crypto transaction trails through mixing services

 crypto_mixing 30 37 29

Cuckoo Smurfing

Exploiting legitimate remittance channels to co-mingle illicit funds

 cuckoo_smurfing 13 25 37

Cycle Detection

Circular transaction flows indicating coordinated laundering rings

 cycle 11 14 21

Dormant Account Activation

Long-inactive accounts suddenly receiving high-volume transactions

 dormant_activation 6 39 37

Fan-in (Mule Collector)

Many accounts funneling funds into a single mule account

 fan_in_mule 23 21 29

Fan-out (Burst)

Single account rapidly dispersing funds to many recipients

 fan_out_burst 18 72 31

Funnel Account

Single account aggregating deposits from dispersed geographic locations

 funnel_account 14 28 25

Layering Chain

Sequential hops through intermediary accounts to obscure fund origin

 layering_chain 6 15 24

Nesting / Payable-Through

Respondent bank clients accessing correspondent banking indirectly

 nesting 3 14 35

Peel Chain

Large crypto amount peeled off in small increments across wallets

 peel_chain 16 19 31

Rapid Movement

Funds moved through multiple accounts within hours to avoid detection

 rapid_movement 20 24 24

Round-Tripping

Funds sent abroad and returned disguised as foreign investment

 round_tripping 28 118 31

Shell Company Network

Funds routed through entities with no real business operations

 shell_company 20 19 33

Smurfing (Structuring)

Coordinated small transactions structured below reporting thresholds

 smurfing 14 22 29

Trade-Based Laundering

Over/under-invoicing of goods and services to move value across borders

 trade_based 8 54 42

Browse Models

Production-grade models registered across the federated graph.

8 models

fgi-autoencoder-anomaly

Production

Variational autoencoder for anomaly detection via reconstruction error on transaction embeddings

Autoencoder pytorch v2
37 pulls 53
View

fgi-gat-multihead

Production

Multi-head attention GAT with 8 attention heads for heterogeneous transaction graphs

GAT pytorch v3
90 pulls 45
View

fgi-gcn-spectral

Production

Spectral GCN with Chebyshev polynomial approximation for transaction graph convolution

GCN tensorflow v4
44 pulls 36
View

fgi-graphsage-inductive

Production

GraphSAGE inductive model for unseen node classification in expanding transaction graphs

GraphSAGE pytorch v4
147 pulls 65
View

fgi-risk-gnn

Production

Federated Graph Intelligence risk scoring GNN — 128-dim node embeddings with 2-layer message passing

GNN pytorch v3
56 pulls 75
View

fgi-temporal-gat

Production

Temporal Graph Attention Network — time-aware attention over transaction sequences

Temporal GNN pytorch v3
45 pulls 75
View

fgi-txn-transformer

Production

Transaction-level Transformer encoder for sequential fraud pattern detection

Transformer pytorch v4
74 pulls 64
View

fgi-xgb-features

Production

XGBoost classifier on graph-derived feature vectors — degree centrality, PageRank, clustering coefficient

XGBoost xgboost v2
40 pulls 45
View

Browse Nodes

Regulatory jurisdictions participating in the federated graph network.

7 regions

US / FinCEN

us_fincen

United States — Financial Crimes Enforcement Network jurisdiction

EU / AMLD

eu_amld

European Union — Anti-Money Laundering Directive jurisdiction

UK / FCA

uk_fca

United Kingdom — Financial Conduct Authority jurisdiction

APAC / FATF

apac_fatf

Asia-Pacific — FATF-aligned regulatory framework

LATAM

latam

Latin America — Regional AML/CFT framework

MENA

mena

Middle East & North Africa — Regional regulatory framework

Africa

africa

Sub-Saharan Africa — Regional AML/CFT framework

Infrastructure for federated intelligence

Everything you need to version, validate, and distribute typologies across the federated graph.

Federated Aggregation

Coordinate typology updates across distributed graph nodes with cryptographically versioned weights.

Immutable Signal Registry

SHA-verified, append-only storage for typology artifacts with granular RBAC and audit trails.

Edge-Ready Distribution

Hot-swap production typologies at the edge. Sub-second propagation across all participating nodes.

Continuous Validation

Automated drift detection and quality gates on every submission before promotion to production.

Quickstart

Get up and running with the official federated-graph Python library.

1

Install the library

pip install federated-graph

Optional extras: [grpc] [redis] [scoring] [all]

2

Configure and initialize a model

import torch
from torch_geometric.data import Data
from federated_graph import FGIConfig
from federated_graph.models import FGIModel

# Create configuration
config = FGIConfig()

# Initialize model
model = FGIModel(in_channels=10, config=config.model)
3

Run a forward pass on graph data

# Prepare graph data
x = torch.randn(100, 10)
edge_index = torch.randint(0, 100, (2, 500))

# Forward pass returns embeddings and risk scores
embeddings, entity_scores, txn_scores = model(x, edge_index)
4

Start federated training

from federated_graph.federation import FederationServer, FederationClient

server = FederationServer(
    num_rounds=config.federation.num_rounds,
    min_clients=config.federation.min_clients,
)

client = FederationClient(
    client_id="client_1",
    model=model,
    config=config,
)

Full documentation at pypi.org/project/federated-graph

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