Update Python version requirements and fix compatibility issues

- Bump minimum Python version from 3.11 to 3.13 across all apps
- Add Python 3.11-3.13 test matrix to CLI workflow
- Document Python 3.11+ requirement in .env.example
- Fix Starlette Broadcast removal with in-process fallback implementation
- Add _InProcessBroadcast class for tests when Starlette Broadcast is unavailable
- Refactor API key validators to read live settings instead of cached values
- Update database models with explicit

apps/zk-circuits/fhe_integration_plan.md

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+# FHE Integration Plan for AITBC
+
+## Candidate Libraries
+
+### 1. Microsoft SEAL (C++ with Python bindings)
+**Pros:**
+- Mature and well-maintained
+- Supports both BFV and CKKS schemes
+- Good performance for ML operations
+- Python bindings available
+- Extensive documentation
+
+**Cons:**
+- C++ dependency complexity
+- Larger binary size
+- Steeper learning curve
+
+**Use Case:** Heavy computational ML workloads
+
+### 2. TenSEAL (Python wrapper for SEAL)
+**Pros:**
+- Pure Python interface
+- Built on top of SEAL
+- Easy integration with existing Python codebase
+- Good for prototyping
+
+**Cons:**
+- Performance overhead
+- Limited to SEAL capabilities
+- Less control over low-level operations
+
+**Use Case:** Rapid prototyping and development
+
+### 3. Concrete ML (Python)
+**Pros:**
+- Designed specifically for ML
+- Supports neural networks
+- Easy model conversion
+- Good performance for inference
+
+**Cons:**
+- Limited to specific model types
+- Newer project, less mature
+- Smaller community
+
+**Use Case:** Neural network inference on encrypted data
+
+## Recommended Approach: Hybrid ZK + FHE
+
+### Phase 1: Proof of Concept with TenSEAL
+- Start with TenSEAL for rapid prototyping
+- Implement basic encrypted inference
+- Benchmark performance
+
+### Phase 2: Production with SEAL
+- Migrate to SEAL for better performance
+- Implement custom optimizations
+- Integrate with existing ZK circuits
+
+### Phase 3: Specialized Solutions
+- Evaluate Concrete ML for neural networks
+- Consider custom FHE schemes for specific use cases
+
+## Integration Architecture
+
+```
+Client Request → ZK Proof Generation → FHE Computation → ZK Result Verification → Response
+```
+
+### Workflow:
+1. Client submits encrypted ML request
+2. ZK circuit proves request validity
+3. FHE computation on encrypted data
+4. ZK circuit proves computation correctness
+5. Return encrypted result with proof