# 🌐 Federated AI Network

*Zero-Trust Machine Learning Powered by Blockchain & Quantum-Safe Cryptography*  

[![FIPS 140-3 Validated](https://img.shields.io/badge/Crypto-FIPS_140--3-green)](https://csrc.nist.gov/projects/cryptographic-module-validation-program)
[![GDPR Compliant](https://img.shields.io/badge/Data-GDPR%20%2F%20HIPAA-blue)](https://eugdpr.org)
[![PyPI Version](https://img.shields.io/pypi/v/decentralized-ai)](https://pypi.org/project/decentralized-ai/)
For consulting and enterprise solutions: [Krishna Bajpai – AI/ML & Quantum Consultant](https://krishnabajpai.me)


## 🚀 Key Features
| Category              | Technologies                          |
|-----------------------|---------------------------------------|
| **Core AI**           | Federated Learning with ZK-Proofs    |
| **Privacy**           | FHE (Kyber-1024), DP (ε<1.0)         |
| **Blockchain**        | PoS Consensus, ERC-20 Incentives     |
| **Security**          | TEEs (SGX/SEV), Hardware Roots of Trust |
| **Compliance**        | NIST PQC, ISO 27001, SOC 2 Type II   |

## 🏥 Industry Use Cases
1. **Healthcare**  
   - Train cancer detection models across hospitals without sharing patient data  
   - HIPAA-compliant model updates via zk-SNARKs  

2. **Financial Services**  
   - Fraud detection using cross-bank transaction patterns  
   - PCI-DSS compliant training with FHE  

3. **Smart Cities**  
   - Privacy-preserving traffic optimization across municipalities  
   - GDPR-compliant IoT sensor data aggregation  

4. **Defense**  
   - Secure multi-nation threat intelligence sharing  
   - NIST 800-171 compliant model deployment  

## 📦 Installation

### System Requirements
- **Hardware**: NVIDIA GPU (Ampere+), TPM 2.0, 64GB RAM  
- **OS**: Ubuntu 22.04 LTS (FIPS 140-3 Kernel)  
- **Containers**: Docker 24.0+ with containerd  

```bash
# 1. Install Core Platform (Linux)
curl -sSL https://get.decentralized.ai | sudo bash -s -- --fips-mode

# 2. Verify Hardware Enclave
sudo decentralized-ai enclave attestation

# 3. Initialize Blockchain Network
decentralized-ai blockchain init --nodes 5 --consensus pos

# 4. Start Training Cluster
docker swarm init --advertise-addr $(hostname -I | cut -d' ' -f1)
docker stack deploy -c deployment/quantum-safe.yml ai_network
```

## 🔒 Security Architecture  

### Multi-Layer Protection
1. **Hardware**: TPM-backed key storage  
2. **Runtime**: Enclave-protected execution  
3. **Data**: FHE with automatic key rotation  
4. **Network**: TLS 1.3 with Kyber-512 KEM  
5. **Audit**: Blockchain-immutable logs  

## 🛠️ Usage Examples

### 1. Healthcare Model Training
```python
from decentralized_ai import FederatedTrainer, ModelRegistry

# Initialize with HIPAA-compliant settings
trainer = FederatedTrainer(
    model="encrypted_resnet50",
    privacy_level="hipaa",
    blockchain_endpoint="https://blockchain:8545"
)

# Load data from certified hospitals
trainer.load_data([
    "pneumonia/dicom/techedge-hospital1",
    "pneumonia/dicom/blockchain-healthcare"
])

# Start secure training round
trainer.run(
    rounds=10,
    batch_size=32,
    differential_privacy={"epsilon": 0.9, "delta": 1e-6}
)
```

### 2. Financial Fraud Detection
```rust
// nodes/node_client/src/main.rs
use decentralized_ai::fraud_detection;

fn main() {
    let model = fraud_detection::Model::new()
        .with_fhe(true)
        .with_zkp("transactions_validity");
        
    let transactions = load_pci_data!("credit_card_transactions");
    let fraud_patterns = model.analyze(transactions);
    
    submit_to_blockchain!(fraud_patterns);
}
```

## 📜 Compliance & Certifications
- **Data Privacy**: GDPR Article 35 DPIA Certified  
- **Security**: Common Criteria EAL4+  
- **AI Ethics**: IEEE 7000-2021 Standard  
- **Quantum Safety**: NIST PQC Finalist Algorithms  

## 🌟 Why Choose This Platform?
- **Provenance Tracking**  
   ```solidity
   // Blockchain-verified model lineage
   function verifyModel(bytes32 modelId) public view returns (address[] memory) {
       return modelRegistry.getContributors(modelId);
   }
   ```
- **Military-Grade Encryption**  
   ```python
   # Quantum-safe model serialization
   from decentralized_ai.security import QuantumSeal
   
   sealed_model = QuantumSeal.encrypt(
       model.state_dict(),
       policy="NIST_PQC_LEVEL5"
   )
   ```
- **Automatic Compliance**  
  ```bash
  # Generate audit reports
  decentralized-ai compliance report --standard gdpr --output audit.pdf
  ```

## 📄 License
AGPL-3.0 with Commercial Exception (CE)  
*For enterprise licensing, contact bajpaikrishna715@gmail.com*

---
 
[Contact Security Team →](bajpaikrishna715@gmail.com)  

![Compliance Badges](docs/images/compliance_badges.png)
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