feat: implement v0.2.0 release features - agent-first evolution

✅ v0.2 Release Preparation:
- Update version to 0.2.0 in pyproject.toml
- Create release build script for CLI binaries
- Generate comprehensive release notes

✅ OpenClaw DAO Governance:
- Implement complete on-chain voting system
- Create DAO smart contract with Governor framework
- Add comprehensive CLI commands for DAO operations
- Support for multiple proposal types and voting mechanisms

✅ GPU Acceleration CI:
- Complete GPU benchmark CI workflow
- Comprehensive performance testing suite
- Automated benchmark reports and comparison
- GPU optimization monitoring and alerts

✅ Agent SDK Documentation:
- Complete SDK documentation with examples
- Computing agent and oracle agent examples
- Comprehensive API reference and guides
- Security best practices and deployment guides

✅ Production Security Audit:
- Comprehensive security audit framework
- Detailed security assessment (72.5/100 score)
- Critical issues identification and remediation
- Security roadmap and improvement plan

✅ Mobile Wallet & One-Click Miner:
- Complete mobile wallet architecture design
- One-click miner implementation plan
- Cross-platform integration strategy
- Security and user experience considerations

✅ Documentation Updates:
- Add roadmap badge to README
- Update project status and achievements
- Comprehensive feature documentation
- Production readiness indicators

🚀 Ready for v0.2.0 release with agent-first architecture

docs/advanced/05_development/fhe-service.md

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+# FHE Service
+
+## Overview
+
+The Fully Homomorphic Encryption (FHE) Service enables encrypted computation on sensitive machine learning data within the AITBC platform. It allows ML inference to be performed on encrypted data without decryption, maintaining privacy throughout the computation process.
+
+## Architecture
+
+### FHE Providers
+- **TenSEAL**: Primary provider for rapid prototyping and production use
+- **Concrete ML**: Specialized provider for neural network inference
+- **Abstract Interface**: Extensible provider system for future FHE libraries
+
+### Encryption Schemes
+- **CKKS**: Optimized for approximate computations (neural networks)
+- **BFV**: Optimized for exact integer arithmetic
+- **Concrete**: Specialized for neural network operations
+
+## TenSEAL Integration
+
+### Context Generation
+```python
+from app.services.fhe_service import FHEService
+
+fhe_service = FHEService()
+context = fhe_service.generate_fhe_context(
+    scheme="ckks",
+    provider="tenseal",
+    poly_modulus_degree=8192,
+    coeff_mod_bit_sizes=[60, 40, 40, 60]
+)
+```
+
+### Data Encryption
+```python
+# Encrypt ML input data
+encrypted_input = fhe_service.encrypt_ml_data(
+    data=[[1.0, 2.0, 3.0, 4.0]],  # Input features
+    context=context
+)
+```
+
+### Encrypted Inference
+```python
+# Perform inference on encrypted data
+model = {
+    "weights": [[0.1, 0.2, 0.3, 0.4]],
+    "biases": [0.5]
+}
+
+encrypted_result = fhe_service.encrypted_inference(
+    model=model,
+    encrypted_input=encrypted_input
+)
+```
+
+## API Integration
+
+### FHE Inference Endpoint
+```bash
+POST /v1/ml-zk/fhe/inference
+{
+  "scheme": "ckks",
+  "provider": "tenseal",
+  "input_data": [[1.0, 2.0, 3.0, 4.0]],
+  "model": {
+    "weights": [[0.1, 0.2, 0.3, 0.4]],
+    "biases": [0.5]
+  }
+}
+
+Response:
+{
+  "fhe_context_id": "ctx_123",
+  "encrypted_result": "encrypted_hex_string",
+  "result_shape": [1, 1],
+  "computation_time_ms": 150
+}
+```
+
+## Provider Details
+
+### TenSEAL Provider
+```python
+class TenSEALProvider(FHEProvider):
+    def generate_context(self, scheme: str, **kwargs) -> FHEContext:
+        # CKKS context for neural networks
+        context = ts.context(
+            ts.SCHEME_TYPE.CKKS,
+            poly_modulus_degree=8192,
+            coeff_mod_bit_sizes=[60, 40, 40, 60]
+        )
+        context.global_scale = 2**40
+        return FHEContext(...)
+
+    def encrypt(self, data: np.ndarray, context: FHEContext) -> EncryptedData:
+        ts_context = ts.context_from(context.public_key)
+        encrypted_tensor = ts.ckks_tensor(ts_context, data)
+        return EncryptedData(...)
+
+    def encrypted_inference(self, model: Dict, encrypted_input: EncryptedData):
+        # Perform encrypted matrix multiplication
+        result = encrypted_input.dot(weights) + biases
+        return result
+```
+
+### Concrete ML Provider
+```python
+class ConcreteMLProvider(FHEProvider):
+    def __init__(self):
+        import concrete.numpy as cnp
+        self.cnp = cnp
+
+    def generate_context(self, scheme: str, **kwargs) -> FHEContext:
+        # Concrete ML context setup
+        return FHEContext(scheme="concrete", ...)
+
+    def encrypt(self, data: np.ndarray, context: FHEContext) -> EncryptedData:
+        encrypted_circuit = self.cnp.encrypt(data, p=15)
+        return EncryptedData(...)
+
+    def encrypted_inference(self, model: Dict, encrypted_input: EncryptedData):
+        # Neural network inference with Concrete ML
+        return self.cnp.run(encrypted_input, model)
+```
+
+## Security Model
+
+### Privacy Guarantees
+- **Data Confidentiality**: Input data never decrypted during computation
+- **Model Protection**: Model weights can be encrypted during inference
+- **Output Privacy**: Results remain encrypted until client decryption
+- **End-to-End Security**: No trusted third parties required
+
+### Performance Characteristics
+- **Encryption Time**: ~10-100ms per operation
+- **Inference Time**: ~100-500ms (TenSEAL)
+- **Accuracy**: Near-native performance for neural networks
+- **Scalability**: Linear scaling with input size
+
+## Use Cases
+
+### Private ML Inference
+```python
+# Client encrypts sensitive medical data
+encrypted_health_data = fhe_service.encrypt_ml_data(health_records, context)
+
+# Server performs diagnosis without seeing patient data
+encrypted_diagnosis = fhe_service.encrypted_inference(
+    model=trained_model,
+    encrypted_input=encrypted_health_data
+)
+
+# Client decrypts result locally
+diagnosis = fhe_service.decrypt(encrypted_diagnosis, private_key)
+```
+
+### Federated Learning
+- Multiple parties contribute encrypted model updates
+- Coordinator aggregates updates without decryption
+- Final model remains secure throughout process
+
+### Secure Outsourcing
+- Cloud providers perform computation on encrypted data
+- No access to plaintext data or computation results
+- Compliance with privacy regulations (GDPR, HIPAA)
+
+## Development Workflow
+
+### Testing FHE Operations
+```python
+def test_fhe_inference():
+    # Setup FHE context
+    context = fhe_service.generate_fhe_context(scheme="ckks")
+
+    # Test data
+    test_input = np.array([[1.0, 2.0, 3.0]])
+    test_model = {"weights": [[0.1, 0.2, 0.3]], "biases": [0.1]}
+
+    # Encrypt and compute
+    encrypted = fhe_service.encrypt_ml_data(test_input, context)
+    result = fhe_service.encrypted_inference(test_model, encrypted)
+
+    # Verify result shape and properties
+    assert result.shape == (1, 1)
+    assert result.context == context
+```
+
+### Performance Benchmarking
+```python
+def benchmark_fhe_performance():
+    import time
+
+    # Benchmark encryption
+    start = time.time()
+    encrypted = fhe_service.encrypt_ml_data(data, context)
+    encryption_time = time.time() - start
+
+    # Benchmark inference
+    start = time.time()
+    result = fhe_service.encrypted_inference(model, encrypted)
+    inference_time = time.time() - start
+
+    return {
+        "encryption_ms": encryption_time * 1000,
+        "inference_ms": inference_time * 1000,
+        "total_ms": (encryption_time + inference_time) * 1000
+    }
+```
+
+## Deployment Considerations
+
+### Resource Requirements
+- **Memory**: 2-8GB RAM per concurrent FHE operation
+- **CPU**: Multi-core support for parallel operations
+- **Storage**: Minimal (contexts cached in memory)
+
+### Scaling Strategies
+- **Horizontal Scaling**: Multiple FHE service instances
+- **Load Balancing**: Distribute FHE requests across nodes
+- **Caching**: Reuse FHE contexts for repeated operations
+
+### Monitoring
+- **Latency Tracking**: End-to-end FHE operation timing
+- **Error Rates**: FHE operation failure monitoring
+- **Resource Usage**: Memory and CPU utilization metrics
+
+## Future Enhancements
+
+- **Hardware Acceleration**: FHE operations on specialized hardware
+- **Advanced Schemes**: Integration with newer FHE schemes (TFHE, BGV)
+- **Multi-Party FHE**: Secure computation across multiple parties
+- **Hybrid Approaches**: Combine FHE with ZK proofs for optimal privacy-performance balance