OpenAI-Assisted Privacy-Preserving Federated Fraud Detection System Implementation

A Coding Implementation of an OpenAI-Assisted Privacy-Preserving Federated Fraud Detection System from Scratch Using Lightweight PyTorch Simulations

This tutorial details a privacy-preserving fraud detection system built using Federated Learning, avoiding heavyweight frameworks. The system simulates ten independent banks training local models on imbalanced transaction data, coordinated via FedAvg, and leverages OpenAI for post-training analysis and reporting.

Federated Learning aims to train models on decentralized data while preserving privacy, a stark contrast to traditional centralized machine learning which requires data consolidation. Real-world deployments often face challenges with non-IID data distribution and communication overhead, potentially leading to model divergence and increased training costs—estimated at $500K - $2M for a fully-fledged production system.

Key Insights

• Dirichlet Partitioning, 2018: Simulates non-IID data distributions across clients, mirroring real-world scenarios where each bank has unique customer behavior.
• FedAvg Algorithm: Enables collaborative model training without sharing raw data, a cornerstone of privacy-preserving machine learning.
• GPT-5.2 for Reporting: Automates the translation of technical results into actionable insights for risk management teams.

Working Example

!pip -q install torch scikit-learn numpy openai
import time, random, json, os, getpass
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score, average_precision_score, accuracy_score
from openai import OpenAI
SEED = 7
random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)
DEVICE = torch.device("cpu")
print("Device:", DEVICE)

X, y = make_classification(
n_samples=60000,
n_features=30,
n_informative=18,
n_redundant=8,
weights=[0.985, 0.015],
class_sep=1.5,
flip_y=0.01,
random_state=SEED
)
X = X.astype(np.float32)
y = y.astype(np.int64)
X_train_full, X_test, y_train_full, y_test = train_test_split(
X, y, test_size=0.2, stratify=y, random_state=SEED
)
server_scaler = StandardScaler()
X_train_full_s = server_scaler.fit_transform(X_train_full).astype(np.float32)
X_test_s = server_scaler.transform(X_test).astype(np.float32)
test_loader = DataLoader(
TensorDataset(torch.from_numpy(X_test_s), torch.from_numpy(y_test)),
batch_size=1024,
shuffle=False
)

def dirichlet_partition(y, n_clients=10, alpha=0.35):
classes = np.unique(y)
idx_by_class = [np.where(y == c)[0] for c in classes]
client_idxs = [[] for _ in range(n_clients)]
for idxs in idx_by_class:
np.random.shuffle(idxs)
props = np.random.dirichlet(alpha * np.ones(n_clients))
cuts = (np.cumsum(props) * len(idxs)).astype(int)
prev = 0
for cid, cut in enumerate(cuts):
client_idxs[cid].extend(idxs[prev:cut].tolist())
prev = cut
return [np.array(ci, dtype=np.int64) for ci in client_idxs]
NUM_CLIENTS = 10
client_idxs = dirichlet_partition(y_train_full, NUM_CLIENTS, 0.35)

Practical Applications

• Financial Institutions: Securely collaborate on fraud detection models without sharing sensitive customer data.
• Pitfall: Ignoring data heterogeneity across clients can lead to biased models and reduced performance; Dirichlet partitioning helps mitigate this.

References:

• https://www.marktechpost.com/2025/12/30/a-coding-implementation-of-an-openai-assisted-privacy-preserving-federated-fraud-detection-system-from-scratch-using-lightweight-pytorch-simulations/