Fractal

Distributed Compute Orchestration and Edge Intelligence Framework

A unified distributed systems framework bridging centralized model orchestration with decentralized edge intelligence.

Overview | Design Case Study | Architecture | Core Paradigms | Repository Topology | Getting Started | Security

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Overview

Fractal is a high-performance distributed computing ecosystem engineered to harvest fragmented consumer compute across mobile edge devices while strictly preserving user privacy and device health. The framework treats edge devices not merely as remote processors, but as autonomous, resource-constrained nodes operating under human-centric constraints.

The ecosystem resolves the computational and memory walls of foundation models through two distinct, unified systems:

1. FractalCore (Central Server & Orchestrator): The Python-based control plane responsible for multi-tenant isolation, dataset segment allocation, model graph compilation/slicing, deterministic federated weight aggregation (FedAvg), compute budgeting, and tokenized reward distribution.
2. FractalAndroid (Edge Compute Node & Application): The native Kotlin/Android execution engine running on mobile hardware. It performs hardware-gated on-device training and memory-mapped inference, dynamically pausing workloads if thermals or battery thresholds are exceeded.

Ecosystem Subsystems

Central Server (FractalCore)	Edge Compute Node (FractalAndroid)
Multi-Tenant Dashboard: Real-time fleet tracking, segment dispatch, and live aggregation telemetry.	Edge Node: On-device training & telemetry.

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System Architecture

Fractal operates on a hierarchical Master-Worker architecture designed to isolate control signals from mathematical payloads.

graph TD
    subgraph ControlPlane ["Control Plane (FractalCore Server)"]
        direction TB
        API["REST Gateway & Auth Controller"]
        TM["Tenant Isolation Manager"]
        TS["Task Scheduler & Segment Dispatcher"]
        FA["Federated Weight Aggregator (FedAvg)"]
        Slicer["Graph Slicer & INT4 Quantizer"]
        Budget["TFLOPs Budget Controller"]
    end

    subgraph DataPlane ["Data Plane (FractalAndroid Edge Nodes)"]
        direction TB
        N1["Android Node A (Layers 0-7 / Worker)"]
        N2["Android Node B (Layers 8-15 / Worker)"]
        N3["Android Node C (Layers 16-23 / Worker)"]
        N4["Android Node D (Layers 24-31 / Worker)"]
    end

    subgraph PersistenceLayer ["Persistence & Ledger"]
        FS[("Firestore Ledger (Tokens / Rewards / Nodes)")]
        Disk[("Physical Data Silos (Bins / Checkpoints / PTE Slices)")]
    end

    %% Control Plane Internal Flow
    API --> TM
    TM --> TS
    TS --> Budget
    FA --> Disk
    Slicer --> Disk
    API --> FS

    %% Network Interactions
    N1 & N2 & N3 & N4 <-->|"REST API / TLS (Task Polling & Weight Uploads)"| API
    N1 -->|"INT8 Activation Stream (Wi-Fi Socket)"| N2
    N2 -->|"INT8 Activation Stream (Wi-Fi Socket)"| N3
    N3 -->|"INT8 Activation Stream (Wi-Fi Socket)"| N4

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Operational Paradigms

Fractal executes distributed machine learning through two foundational paradigms:

1. Federated Learning (On-Device Training)

In the federated learning lifecycle, raw training datasets are never transferred to the central server. The server partitions data indices into segment bins, dispatches task descriptors to registered Android nodes, and waits for computed checkpoint deltas (.ckpt).

sequenceDiagram
    participant Tenant as Tenant / Administrator
    participant Server as FractalCore Server
    participant Node as FractalAndroid Node
    participant Firestore as Firestore Registry

    Tenant->>Server: Initialize Session (Dataset + TFLOP Budget)
    Server->>Server: Partition Dataset into Binary Segment Bins
    Note over Server: Queue Tasks in Tenant Silo

    Node->>Server: GET /api/task/current (Hardware Telemetry Proof)
    Server-->>Node: ActiveTask (Model URL + Data Bin URL + Hyperparams)

    Node->>Node: Evaluate Telemetry Permit (Battery >= 50%, Thermals <= 40C)
    Node->>Server: Download Model Weights (.tflite) + Binary Bins
    Node->>Node: Execute Local Gradient Descent (TFLite Engine)
    Node->>Server: POST /api/model/upload (Checkpoint Delta .ckpt)

    Server->>Firestore: Credit Liquid MBs to Node Device ID
    Server->>Server: Federated Averaging (FedAvg Summation)
    Server->>Server: Increment Global Model Round

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2. Decentralized Pipeline Parallelism (Large Foundation Model Inference)

To execute foundation models (such as Llama 3 8B or TinyLlama) across edge devices without triggering the Android Low Memory Killer (LMK), Fractal Core slices the monolithic model into memory-bounded partitions (layer_[N].pte <= 150MB). Each Android node memory-maps (mmap) an assigned partition and transmits quantized activation tensors across an ad-hoc local mesh.

flowchart LR
    subgraph CoreSlicer ["FractalCore Compiler"]
        M16["Monolithic 16GB FP16 Checkpoint"] --> Ingest["Ingestion & Block Isolation"]
        Ingest --> Quant["INT4 Weight Quantization (torchao)"]
        Quant --> Trace["ATen Dialect Graph Trace (torch.export)"]
        Trace --> Lower["XNNPACK Hardware Delegation"]
        Lower --> Out["32x Mapped Partitions (layer_N.pte <= 150MB)"]
    end

    subgraph EdgeMesh ["Android Compute Mesh"]
        Out -.->|"Deploy Layer Partition"| NodeA["Node A (Input / Layer 0-7)"]
        NodeA -->|"INT8 Activations (<= 4.1KB)"| NodeB["Node B (Layer 8-15)"]
        NodeB -->|"INT8 Activations (<= 4.1KB)"| NodeC["Node C (Layer 16-23)"]
        NodeC -->|"INT8 Activations (<= 4.1KB)"| NodeD["Node D (Output / Layer 24-31)"]
    end

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Global System Contracts

The ecosystem maintains strict operational boundaries governed by mathematical contracts:

Contract	Target Subsystem	Strict Specification	Failure Behavior
Weight Contract	Model Slicing & Storage	Binary partition (.pte / .tflite) strictly $\le$ 150MB	Slicer compilation rejected
Activation Contract	Pipeline Inter-Node Network	4096-dim INT8 tensor + 1 Float scale strictly $\le$ 4,100 bytes	Network frame dropped and re-requested
Telemetry Permit	On-Device Execution Gate	Battery $\ge$ 50%, Charging == True, Battery Temp $\le$ 40.0 C	Training paused immediately
Authentication Contract	REST Ingress Gateway	X-Auth-Token required for all tenant and administrative routes	HTTP 401 / 403 response
Upload Validation	Federated Aggregation Gate	task_Id must match an active unconsumed dispatch record	HTTP 400 Unknown task_Id

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Repository Topology

The Fractal ecosystem is partitioned across dedicated modular repositories:

.
|-- FractalApp/
|   `-- FractalAndroid/              # Client Application & Compute Node (Kotlin)
|       |-- app/src/main/java/       # Native MVVM Architecture
|       |   |-- AppBackend/          # Telemetry, TFLite Engine, Checkpointing, DAOs
|       |   `-- AppFrontend/         # Telemetry Dashboard, Insights & Settings
|       |-- build.gradle.kts         # Android Build Configuration (Kotlin DSL)
|       `-- docs/                    # Architecture diagrams and specifications
|
|-- FractalCore/                     # Server & Control Plane (Python)
|   |-- src/
|   |   |-- fractal_server/          # Multi-Tenant Flask API & Federated Aggregator
|   |   `-- inference-model-maker/   # Slicer Compiler & Model Partitioning Pipeline
|   |-- scripts/                     # Operational Sweepers, Testers & Reward Audits
|   |-- docs/                        # API schemas, deployment, and security specs
|   |-- Dockerfile                   # Production Container Definition
|   `-- docker-compose.yml           # Multi-Container Orchestration Stack
|
|-- docs/                            # Unified Project Whitepapers & Reports
`-- private.envs/                    # Private Environment Configurations & Service Keys

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Getting Started

Prerequisites

• Python: Version 3.10 or higher
• Android Studio: Version 2023.3.1 (Jellyfish) or newer
• Android Device: Physical device running Android API 24+ (ARM64 recommended)
• Google Cloud Platform: Firebase Project with Firestore enabled

Cloning with Submodules

To initialize the entire workspace including all submodule repositories:

git clone --recursive https://github.com/Fractal-Compute-Orchestrations/FractalWorkspace.git

If the repository was cloned without --recursive, initialize submodules manually:

git submodule update --init --recursive

Initializing the Orchestrator (FractalCore)

cd FractalCore
cp .env.example .env

# Launch production server via Docker Compose
docker-compose up --build -d

# Verify server status
curl http://localhost:5000/api/admin/tenants

Initializing the Edge Client (FractalAndroid)

cd FractalApp/FractalAndroid

# Build project with Gradle
./gradlew assembleDebug

# Install on connected physical device
./gradlew installDebug

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Security and Privacy

• Zero Data Ingress: The central server never ingests, stores, or processes raw user datasets. Only compiled mathematical weight checkpoints are transmitted.
• Stateless Token Authentication: Authentication is enforced exclusively through the X-Auth-Token request header. Tokens are cryptographically generated (secrets.token_hex(32)) and isolated from browser cookies to eliminate cross-tab session leakage.
• Physical Tenant Sandboxing: Multi-tenancy is enforced at the storage engine level. Each tenant's datasets, bins, and intermediate checkpoints are sandboxed in isolated directory trees (data/tenants/{username}/).
• Replay Protection: Checkpoint uploads require a valid, non-expired, single-use task_Id. Stale or duplicate uploads are rejected at the ingress gate.

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Governance & Licensing

Fractal is architected and owned by Ahmad Hassan (B-Ted).

• Code of Conduct: See CODE_OF_CONDUCT.md
• Contribution Guidelines: See CONTRIBUTING.md
• Security Disclosure: See SECURITY.md
• License: Proprietary & Source-Available under the Fractal Proprietary Source-Available & Non-Commercial Restrictive License v3.0. All Rights Reserved. Commercial use strictly prohibited without written authorization.