decentralized-ai 1.0.1

Decentralized AI Training Platform

🌐 Federated AI Network

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

For consulting and enterprise solutions: Krishna Bajpai – AI/ML & Quantum Consultant

🚀 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

# 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

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

// 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

  // Blockchain-verified model lineage
  function verifyModel(bytes32 modelId) public view returns (address[] memory) {
      return modelRegistry.getContributors(modelId);
  }
  

• Military-Grade Encryption

  # Quantum-safe model serialization
  from decentralized_ai.security import QuantumSeal
  
  sealed_model = QuantumSeal.encrypt(
      model.state_dict(),
      policy="NIST_PQC_LEVEL5"
  )
  

• Automatic Compliance

  # Generate audit reports
  decentralized-ai compliance report --standard gdpr --output audit.pdf
  

📄 License

AGPL-3.0 with Commercial Exception (CE)
For enterprise licensing, contact [e-mail]

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