🧠Cifer Fraud Detection Mini Model
(cifer-fraud-detection-mini-model)
🧾 Overview
This is a compact binary classifier trained to detect fraudulent transactions using the Cifer Mini Fraud Detection Dataset. It serves as an example model for demonstrating fully encrypted training using Fully Homomorphic Encryption (FHE).
The model architecture is lightweight and optimized for short training cycles, making it ideal for testing encryption workflows, verifying pipeline correctness, or onboarding new users. While not production-scale, it reflects the same privacy-first principles as larger models in the Cifer ecosystem: decentralized learning, secure computation, and zero raw data exposure.
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