# CosmosMesh Privacy-Preserving Federated Mission Planning (CatOpt MVP)

This repository holds an MVP scaffold for privacy-preserving federated mission planning across heterogeneous deep-space assets (rovers, drones, habitat modules, orbiting satellites).

Key idea
- Map CosmosMesh primitives to a lightweight CatOpt-style representation to enable plug-and-play adapters and cross-domain experimentation without heavy dependencies.

Core MVP outline
- Objects = LocalProblems (per-asset planning tasks)
- Morphisms = SharedVariables / DualVariables (data channels with versioned contracts)
- Functors = Adapters translating device-specific models into canonical CosmosMesh problems
- Lightweight transport (TLS-based) and a tiny ADMM-lite solver per asset
- Graph-of-Contracts registry for schemas and per-message metadata to support audits and replay protection

What’s included in this patch
- Added a minimal CatOpt bridge module: src/cosmosmesh_privacy_preserving_federated/catopt_bridge.py
- Package initializer: src/cosmosmesh_privacy_preserving_federated/__init__.py
- Lightweight unit test: tests/test_catopt_bridge.py
- Simple usage example via a RoundTrip encoding path
- Basic README describing MVP approach and how to extend

How to run tests
- Ensure Python 3.8+ environment
- Run: bash test.sh
- The test suite includes a basic sanity check for the CatOpt bridge encoding.

Next steps (high level)
- Wire two starter adapters (rover planner and habitat life-support) into the bridge
- Implement a small ADMM-lite solver per asset and a toy end-to-end round trip
- Add delta-sync protocol scaffolding and an auditable reconciliation log

This work aligns with the roadmap described in AGENTS.md and is designed to be extended incrementally.