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).