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Update Datasets

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  1. README.md +11 -7
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  ## 🧠 Overview
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  The **Cifer-Fraud-Detection-Dataset-AF** is a high-fidelity, fully synthetic dataset created to support the development and benchmarking of privacy-preserving, federated, and decentralized machine learning systems in financial fraud detection.
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- This dataset draws structural inspiration from the **PaySim simulator,** which was built using aggregated mobile money transaction data from a real financial provider operating in 14+ countries. Cifer extends this format by scaling it to **6 million samples,** optimizing for **federated learning environments,** and validating performance against real-world datasets.
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  > ### Accuracy Benchmark:
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  > Cifer-trained models on this dataset reach **99.93% accuracy,** benchmarked against real-world fraud datasets with **99.98% baseline accuracy**—providing high-fidelity behavior for secure, distributed ML research.
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  ---
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  # 📁 File Organization
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- Total Rows: **6,000,000**
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- Split into 4 folders/files for large-scale and federated learning scenarios:
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- - `Cifer-Fraud-Detection-Dataset-AF-part-1-4.csv` → 1.5M rows
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- - `Cifer-Fraud-Detection-Dataset-AF-part-2-4.csv` → 1.5M rows
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- - `Cifer-Fraud-Detection-Dataset-AF-part-3-4.csv` → 1.5M rows
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- - `Cifer-Fraud-Detection-Dataset-AF-part-4-4.csv` → 1.5M rows
 
 
 
 
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  Format: `.csv` (optionally `.parquet` or `.json` upon request)
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  ## 🧠 Overview
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  The **Cifer-Fraud-Detection-Dataset-AF** is a high-fidelity, fully synthetic dataset created to support the development and benchmarking of privacy-preserving, federated, and decentralized machine learning systems in financial fraud detection.
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+ This dataset draws structural inspiration from the **PaySim simulator,** which was built using aggregated mobile money transaction data from a real financial provider operating in 14+ countries. Cifer extends this format by scaling it to **12 million samples,** optimizing for **federated learning environments,** and validating performance against real-world datasets.
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  > ### Accuracy Benchmark:
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  > Cifer-trained models on this dataset reach **99.93% accuracy,** benchmarked against real-world fraud datasets with **99.98% baseline accuracy**—providing high-fidelity behavior for secure, distributed ML research.
 
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  ---
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  # 📁 File Organization
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+ Total Rows: **12,000,000**
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+ Split into 8 folders/files for large-scale and federated learning scenarios:
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-1-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-2-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-3-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-4-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-5-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-6-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-7-8.csv` → 1.5M rows
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+ - `Cifer-Fraud-Detection-Dataset-AF-part-8-8.csv` → 1.5M rows
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  Format: `.csv` (optionally `.parquet` or `.json` upon request)
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