{"id":208,"date":"2025-12-31T03:19:28","date_gmt":"2025-12-30T19:19:28","guid":{"rendered":"https:\/\/connectword.dpdns.org\/?p=208"},"modified":"2025-12-31T03:19:28","modified_gmt":"2025-12-30T19:19:28","slug":"a-coding-implementation-of-an-openai-assisted-privacy-preserving-federated-fraud-detection-system-from-scratch-using-lightweight-pytorch-simulations","status":"publish","type":"post","link":"https:\/\/connectword.dpdns.org\/?p=208","title":{"rendered":"A Coding Implementation of an OpenAI-Assisted Privacy-Preserving Federated Fraud Detection System from Scratch Using Lightweight PyTorch Simulations"},"content":{"rendered":"

In this tutorial, we demonstrate how we simulate a privacy-preserving fraud detection system using Federated Learning without relying on heavyweight frameworks or complex infrastructure. We build a clean, CPU-friendly setup that mimics ten independent banks, each training a local fraud-detection model on its own highly imbalanced transaction data. We coordinate these local updates through a simple FedAvg aggregation loop, allowing us to improve a global model while ensuring that no raw transaction data ever leaves a client. Alongside this, we integrate OpenAI to support post-training analysis and risk-oriented reporting, demonstrating how federated learning outputs can be translated into decision-ready insights. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n

\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
!pip -q install torch scikit-learn numpy openai\n\n\nimport time, random, json, os, getpass\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import roc_auc_score, average_precision_score, accuracy_score\nfrom openai import OpenAI\n\n\nSEED = 7\nrandom.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)\n\n\nDEVICE = torch.device(\"cpu\")\nprint(\"Device:\", DEVICE)<\/code><\/pre>\n<\/div>\n<\/div>\n
We set up the execution environment and import all required libraries for data generation, modeling, evaluation, and reporting. We also fix random seeds and the device configuration to ensure our federated simulation remains deterministic and reproducible on CPU. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n
\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
X, y = make_classification(\n   n_samples=60000,\n   n_features=30,\n   n_informative=18,\n   n_redundant=8,\n   weights=[0.985, 0.015],\n   class_sep=1.5,\n   flip_y=0.01,\n   random_state=SEED\n)\n\n\nX = X.astype(np.float32)\ny = y.astype(np.int64)\n\n\nX_train_full, X_test, y_train_full, y_test = train_test_split(\n   X, y, test_size=0.2, stratify=y, random_state=SEED\n)\n\n\nserver_scaler = StandardScaler()\nX_train_full_s = server_scaler.fit_transform(X_train_full).astype(np.float32)\nX_test_s = server_scaler.transform(X_test).astype(np.float32)\n\n\ntest_loader = DataLoader(\n   TensorDataset(torch.from_numpy(X_test_s), torch.from_numpy(y_test)),\n   batch_size=1024,\n   shuffle=False\n)\n<\/code><\/pre>\n<\/div>\n<\/div>\n
We generate a highly imbalanced, credit-card-like fraud dataset & split it into training & test sets. We standardize the server-side data and prepare a global test loader that allows us to consistently evaluate the aggregated model after each federated round. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n
\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
def dirichlet_partition(y, n_clients=10, alpha=0.35):\n   classes = np.unique(y)\n   idx_by_class = [np.where(y == c)[0] for c in classes]\n   client_idxs = [[] for _ in range(n_clients)]\n   for idxs in idx_by_class:\n       np.random.shuffle(idxs)\n       props = np.random.dirichlet(alpha * np.ones(n_clients))\n       cuts = (np.cumsum(props) * len(idxs)).astype(int)\n       prev = 0\n       for cid, cut in enumerate(cuts):\n           client_idxs[cid].extend(idxs[prev:cut].tolist())\n           prev = cut\n   return [np.array(ci, dtype=np.int64) for ci in client_idxs]\n\n\nNUM_CLIENTS = 10\nclient_idxs = dirichlet_partition(y_train_full, NUM_CLIENTS, 0.35)\n\n\ndef make_client_split(X, y, idxs):\n   Xi, yi = X[idxs], y[idxs]\n   if len(np.unique(yi)) < 2:\n       other = np.where(y == (1 - yi[0]))[0]\n       add = np.random.choice(other, size=min(10, len(other)), replace=False)\n       Xi = np.concatenate([Xi, X[add]])\n       yi = np.concatenate([yi, y[add]])\n   return train_test_split(Xi, yi, test_size=0.15, stratify=yi, random_state=SEED)\n\n\nclient_data = [make_client_split(X_train_full, y_train_full, client_idxs[c]) for c in range(NUM_CLIENTS)]\n\n\ndef make_client_loaders(Xtr, ytr, Xva, yva):\n   sc = StandardScaler()\n   Xtr_s = sc.fit_transform(Xtr).astype(np.float32)\n   Xva_s = sc.transform(Xva).astype(np.float32)\n   tr = DataLoader(TensorDataset(torch.from_numpy(Xtr_s), torch.from_numpy(ytr)), batch_size=512, shuffle=True)\n   va = DataLoader(TensorDataset(torch.from_numpy(Xva_s), torch.from_numpy(yva)), batch_size=512)\n   return tr, va\n\n\nclient_loaders = [make_client_loaders(*cd) for cd in client_data]<\/code><\/pre>\n<\/div>\n<\/div>\n
We simulate realistic non-IID behavior by partitioning the training data across ten clients using a Dirichlet distribution. We then create independent client-level train and validation loaders, ensuring that each simulated bank operates on its own locally scaled data. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n
\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
class FraudNet(nn.Module):\n   def __init__(self, in_dim):\n       super().__init__()\n       self.net = nn.Sequential(\n           nn.Linear(in_dim, 64),\n           nn.ReLU(),\n           nn.Dropout(0.1),\n           nn.Linear(64, 32),\n           nn.ReLU(),\n           nn.Dropout(0.1),\n           nn.Linear(32, 1)\n       )\n   def forward(self, x):\n       return self.net(x).squeeze(-1)\n\n\ndef get_weights(model):\n   return [p.detach().cpu().numpy() for p in model.state_dict().values()]\n\n\ndef set_weights(model, weights):\n   keys = list(model.state_dict().keys())\n   model.load_state_dict({k: torch.tensor(w) for k, w in zip(keys, weights)}, strict=True)\n\n\n@torch.no_grad()\ndef evaluate(model, loader):\n   model.eval()\n   bce = nn.BCEWithLogitsLoss()\n   ys, ps, losses = [], [], []\n   for xb, yb in loader:\n       logits = model(xb)\n       losses.append(bce(logits, yb.float()).item())\n       ys.append(yb.numpy())\n       ps.append(torch.sigmoid(logits).numpy())\n   y_true = np.concatenate(ys)\n   y_prob = np.concatenate(ps)\n   return {\n       \"loss\": float(np.mean(losses)),\n       \"auc\": roc_auc_score(y_true, y_prob),\n       \"ap\": average_precision_score(y_true, y_prob),\n       \"acc\": accuracy_score(y_true, (y_prob >= 0.5).astype(int))\n   }\n\n\ndef train_local(model, loader, lr):\n   opt = torch.optim.Adam(model.parameters(), lr=lr)\n   bce = nn.BCEWithLogitsLoss()\n   model.train()\n   for xb, yb in loader:\n       opt.zero_grad()\n       loss = bce(model(xb), yb.float())\n       loss.backward()\n       opt.step()<\/code><\/pre>\n<\/div>\n<\/div>\n
We define the neural network used for fraud detection along with utility functions for training, evaluation, and weight exchange. We implement lightweight local optimization and metric computation to keep client-side updates efficient and easy to reason about. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n
\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
def fedavg(weights, sizes):\n   total = sum(sizes)\n   return [\n       sum(w[i] * (s \/ total) for w, s in zip(weights, sizes))\n       for i in range(len(weights[0]))\n   ]\n\n\nROUNDS = 10\nLR = 5e-4\n\n\nglobal_model = FraudNet(X_train_full.shape[1])\nglobal_weights = get_weights(global_model)\n\n\nfor r in range(1, ROUNDS + 1):\n   client_weights, client_sizes = [], []\n   for cid in range(NUM_CLIENTS):\n       local = FraudNet(X_train_full.shape[1])\n       set_weights(local, global_weights)\n       train_local(local, client_loaders[cid][0], LR)\n       client_weights.append(get_weights(local))\n       client_sizes.append(len(client_loaders[cid][0].dataset))\n   global_weights = fedavg(client_weights, client_sizes)\n   set_weights(global_model, global_weights)\n   metrics = evaluate(global_model, test_loader)\n   print(f\"Round {r}: {metrics}\")<\/code><\/pre>\n<\/div>\n<\/div>\n
We orchestrate the federated learning process by iteratively training local client models and aggregating their parameters using FedAvg. We evaluate the global model after each round to monitor convergence and understand how collective learning improves fraud detection performance. Check out the\u00a0Full Codes here<\/a><\/strong>.<\/p>\n
\n
\n
\n
\n
<\/div>\n
<\/div>\n
<\/div>\n<\/div>\n
Copy Code<\/span>Copied<\/span>Use a different Browser<\/span><\/a><\/div>\n<\/div>\n
OPENAI_API_KEY = getpass.getpass(\"Enter OPENAI_API_KEY (input hidden): \").strip()\n\n\nif OPENAI_API_KEY:\n   os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n   client = OpenAI()\n\n\n   summary = {\n       \"rounds\": ROUNDS,\n       \"num_clients\": NUM_CLIENTS,\n       \"final_metrics\": metrics,\n       \"client_sizes\": [len(client_loaders[c][0].dataset) for c in range(NUM_CLIENTS)],\n       \"client_fraud_rates\": [float(client_data[c][1].mean()) for c in range(NUM_CLIENTS)]\n   }\n\n\n   prompt = (\n       \"Write a concise internal fraud-risk report.n\"\n       \"Include executive summary, metric interpretation, risks, and next steps.nn\"\n       + json.dumps(summary, indent=2)\n   )\n\n\n   resp = client.responses.create(model=\"gpt-5.2\", input=prompt)\n   print(resp.output_text)<\/code><\/pre>\n<\/div>\n<\/div>\n
We transform the technical results into a concise analytical report using an external language model. We securely accept the API key via keyboard input and generate decision-oriented insights that summarize performance, risks, and recommended next steps.<\/p>\n
In conclusion, we showed how to implement federated learning from first principles in a Colab notebook while remaining stable, interpretable, and realistic. We observed how extreme data heterogeneity across clients influences convergence and why careful aggregation and evaluation are critical in fraud-detection settings. We also extended the workflow by generating an automated risk-team report, demonstrating how analytical results can be translated into decision-ready insights. At last, we presented a practical blueprint for experimenting with federated fraud models that emphasizes privacy awareness, simplicity, and real-world relevance.<\/p>\n
\n
Check out the\u00a0Full Codes here<\/a><\/strong>.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0100k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0now you can join us on telegram as well.<\/a><\/strong><\/p>\n
The post A Coding Implementation of an OpenAI-Assisted Privacy-Preserving Federated Fraud Detection System from Scratch Using Lightweight PyTorch Simulations<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"
In this tutorial, we demonstra…<\/p>\n","protected":false},"author":1,"featured_media":29,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-208","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/posts\/208","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=208"}],"version-history":[{"count":0,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/posts\/208\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=\/wp\/v2\/media\/29"}],"wp:attachment":[{"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=208"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=208"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/connectword.dpdns.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=208"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}