Create README.md

# 🧠 Cifer Fraud Detection Model
`(cifer-fraud-detection-k1-a)`

## 🧾 Overview

This model is a binary classifier trained to detect fraudulent transactions using the **Cifer Fraud Detection Dataset** (6 million synthetic rows). It is designed to operate in **federated learning environments,** where data is split across clients or organizations without centralized access.

This model was trained on **6 million synthetic rows,** split into **four partitions of 1.5 million records each.** You can train this model **independently across the four dataset partitions,** then **aggregate the results using FedAvg (Federated Averaging)** to achieve performance **comparable to centralized training**—as validated in Cifer’s internal lab benchmarks.

This model is part of Cifer’s **laboratory-validated framework for privacy-preserving machine learning,** enabling secure, consent-first collaboration without exposing raw data. It is fully compatible with **Cifer’s no-code workspace** and **federated orchestration engine.**

README.md

@@ -0,0 +1,96 @@
+---
+license: apache-2.0
+datasets:
+- CiferAI/Cifer-Fraud-Detection-Dataset-AF
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+---
+
+# 🧠 Cifer Fraud Detection Model
+`(cifer-fraud-detection-k1-a)`
+
+## 🧾 Overview
+
+This model is a binary classifier trained to detect fraudulent transactions using the **Cifer Fraud Detection Dataset** (6 million synthetic rows). It is designed to operate in **federated learning environments,** where data is split across clients or organizations without centralized access.
+
+This model was trained on **6 million synthetic rows,** split into **four partitions of 1.5 million records each.** You can train this model **independently across the four dataset partitions,** then **aggregate the results using FedAvg (Federated Averaging)** to achieve performance **comparable to centralized training**—as validated in Cifer’s internal lab benchmarks.
+
+This model is part of Cifer’s **laboratory-validated framework for privacy-preserving machine learning,** enabling secure, consent-first collaboration without exposing raw data. It is fully compatible with **Cifer’s no-code workspace** and **federated orchestration engine.**
+
+---
+
+## 📊 Training Data
+
+- **Dataset:** CiferAI/Cifer-Fraud-Detection-Dataset-AF
+- **Total rows:** 6,000,000 (split into 4 federated parts)
+- **Type:** Fully synthetic tabular data modeled after real-world financial fraud scenarios
+- **Fields:** transaction type, amount, sender/receiver balance, fraud flags, and step-based timestamps
+- **Generated with:** Cifer Simulation Engine, modeled after the PaySim simulator
+
+---
+
+## 🧠 Model Architecture
+
+- **Framework:** TensorFlow / Keras
+- **Architecture:** Multi-layer Perceptron (MLP)
+- **Layers:**
+  - Input Layer (shape = number of features)
+  - Dense(64, activation="relu")
+  - Dense(32, activation="relu")
+  - Dense(2, activation="softmax")
+- **Loss Function:** `sparse_categorical_crossentropy`
+- **Optimizer:** `adam`
+- **Output:** Fraud probability classification (0 = normal, 1 = fraud)
+
+---
+
+## ✅ Use Cases
+
+- Fraud detection in fintech, mobile money, and digital banking
+- Federated AI research across decentralized clients
+- Privacy-preserving machine learning benchmarking
+- Fairness and bias mitigation testing under distribution shift
+- Integration with Cifer's federated orchestration engine and no-code workspace
+
+---
+
+## 📈 Performance
+
+Trained on a synthetic dataset **benchmarked against real-world financial logs.**
+This model achieves **99.93% accuracy,** closely matching the **99.98% benchmark** of models trained on real financial data.
+Performance consistency is preserved across federated nodes when using **FedAvg** aggregation.
+
+---
+
+## 🔐 Privacy & Federated Context
+
+- Designed for federated training across 4 dataset partitions
+- No raw data sharing between clients or central servers
+- Supports Cifer’s asynchronous training and client coordination
+- Compatible with Cifer’s blockchain-based contribution tracking and aggregation module
+
+---
+
+## 🔧 File Info
+
+- **Format:** `.h5` (Keras model file)
+- **Input:** Preprocessed numeric tabular data (StandardScaler + LabelEncoded type)
+- **Target:** `isFraud` binary label (0 or 1)
+- **Recommended loader:** `keras.models.load_model("client_model.h5")`
+
+---
+
+## 📜 License
+Apache 2.0
+
+---
+
+## 🙌 Citation
+If you use this model or dataset in your work, please cite:
+> CiferAI (2025). Cifer Fraud Detection Dataset & Federated Model – Privacy-Preserving AI for Financial Risk.
+
+---