16 lines
795 B
Markdown
16 lines
795 B
Markdown
# Interplanetary Edge Orchestrator: Privacy-Preserving Federated Optimization
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This repository contains a minimal, working Python simulation of a privacy-preserving
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federated optimization layer designed for fleets of robotics operating with offline-first
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connectivity in space habitats. It demonstrates a simple, DP-friendly aggregation of local
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updates from multiple clients to form a global model.
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Usage highlights:
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- Lightweight Client and Server implemented in Python.
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- Local data training using gradient descent for linear regression.
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- Privacy-preserving flavor via optional noise on aggregated updates.
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- Offline-first capability via local update caching (non-connected clients save updates to disk).
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How to run tests:
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- This repository provides a test script via test.sh (see below).
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