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Problem: Space habitats and deep-space missions rely on fleets of autonomous robots (rover, aerial drones, maintenance bots) and stationary modules that must operate with intermittent or no connectivity. Centralized planning is infeasible due to late
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README.md

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).