FractalAndroid

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![FractalAndroid Header](docs/assets/header_13.png)

# FractalAndroid
### Resource-Aware Edge Compute Node and Mobile Execution Engine

[![Kotlin Version](https://img.shields.io/badge/Kotlin-1.9.0%2B-black?style=for-the-badge&logo=kotlin)](https://kotlinlang.org)
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**FractalAndroid is the distributed compute client and edge execution runtime for the Fractal decentralized intelligence framework.**

[Overview](#overview) | [Edge Benchmarks](#edge-performance-benchmarks--transmission-efficiency) | [Design Showcase](#mobile-ui-design--hardware-showcase) | [App UI Tour](#mobile-application-interface) | [WiFi Bank Rewards](#wifi-bank--bandwidth-rewards) | [Design Case Study](https://www.behance.net/gallery/221459335/Fractal) | [Architecture](#system-architecture) | [Telemetry Gating](#hardware-telemetry-gating) | [Lifecycle](#execution-lifecycle) | [Build & Setup](#development-and-build) | [References](#related-works--edge-computing-references)

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## Overview

### Executive Summary: Core Purpose & Bottom Line

> **The Problem:** Running machine learning workloads on consumer smartphones traditionally causes rapid battery drain, thermal throttling (>42°C), user UI lag, and process termination by the Android Low Memory Killer (LMK).
>
> **The FractalAndroid Achievement:**
> * **Hardware-Gated Compute**: Automatically pauses execution if SoC temperature exceeds 40°C, battery drops below 80%, or unmetered Wi-Fi is disconnected.
> * **Zero LMK Evictions**: Directly streams 4-bit partitioned `.pte` sub-graphs into unified LPDDR memory via `mmap`, avoiding JVM Garbage Collection overhead and keeping RAM usage well beneath device thresholds.
> * **85.6% Less RF Power**: Slashes parameter upload time from 321.2s to 46.2s over 10 Mbps links, eliminating radio power amplifier heating and conserving device battery health.

FractalAndroid turns Android mobile devices into autonomous, privacy-preserving compute nodes within the Fractal distributed network. Operating in coordination with the central server (**FractalCore**), the application executes localized machine learning workloads, including on-device federated training (TFLite) and partitioned batch dataset training, without exposing user data or degrading host device performance.

The client is designed