# 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).

Privacy controls
- The system supports DP-friendly clipping of updates to bound sensitivity.
- Client.train accepts an optional clip_norm parameter (default None). If provided, per-update deltas are clipped to have L2 norm at most clip_norm before sending to the server.
- Server.aggregate also supports an optional clip_norm parameter to clip all incoming updates prior to averaging, providing an additional privacy safeguard.
- You can combine clipping with Gaussian noise (noise_scale) for stronger privacy guarantees.

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