# Interplanetary Edge Orchestrator: Privacy-Preserving Federated Optimization

This repository contains a minimal, working Python simulation of a privacy-preserving
federated optimization layer designed for fleets of robotics operating with offline-first
connectivity in space habitats. It demonstrates a simple, DP-friendly aggregation of local
updates from multiple clients to form a global model.

Usage highlights:
- Lightweight Client and Server implemented in Python.
- Local data training using gradient descent for linear regression.
- Privacy-preserving flavor via optional noise on aggregated updates.
- Offline-first capability via local update caching (non-connected clients save updates to disk).

How to run tests:
- This repository provides a test script via test.sh (see below).