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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).

How to run tests:

  • This repository provides a test script via test.sh (see below).