https://github.com/asmuelle/orchard
Decentralized, privacy-preserving swarm-intelligence network powered by idle Apple devices (Foundation Models + Neural Engine).
https://github.com/asmuelle/orchard
Last synced: about 18 hours ago
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Decentralized, privacy-preserving swarm-intelligence network powered by idle Apple devices (Foundation Models + Neural Engine).
- Host: GitHub
- URL: https://github.com/asmuelle/orchard
- Owner: asmuelle
- License: other
- Created: 2026-06-20T10:06:52.000Z (17 days ago)
- Default Branch: main
- Last Pushed: 2026-06-20T11:12:17.000Z (17 days ago)
- Last Synced: 2026-06-20T12:12:39.671Z (17 days ago)
- Language: Swift
- Homepage: https://asmuelle.github.io/orchard/
- Size: 43 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Agents: AGENTS.md
Awesome Lists containing this project
README
# 🌳 Orchard
[](https://github.com/asmuelle/orchard/actions/workflows/ci.yml)
> A decentralized, privacy-preserving swarm-intelligence network powered entirely by idle Apple devices.
Orchard turns millions of idle iPhones, iPads, and Macs into a cooperative supercomputing
network. By coordinating the on-device **Apple Foundation Models (AFM)** and **Apple Neural
Engines (ANE)** of participating devices, Orchard tackles massive, complex problems — from
disease modeling to decentralized web indexing — while keeping **100% of raw user data on
the device**.
---
## Why Orchard
| Conventional cloud AI | Orchard |
| --- | --- |
| Energy-intensive centralized datacenters | Idle silicon you already own |
| Per-token API fees | Zero marginal compute cost |
| Raw data leaves the device | Raw data never leaves the device |
| Owned by a single company | Collectively owned knowledge graph |
## The Four Technical Pillars
1. **Local Node Execution** — Each device is a node running native on-device inference via the
[Foundation Models framework](https://developer.apple.com/documentation/foundationmodels)
on the Neural Engine. Opportunistic: runs only when **plugged in, on Wi-Fi, and idle**.
2. **Local Micro-Swarms** — Devices on the same LAN shard high-parameter models across their
unified memories using MLX-style peer-to-peer clustering.
3. **Global Agentic Workflows** — A Task Router (on Private Cloud Compute) fragments massive
problems into micro-prompts; millions of edge agents process chunks and return structured
output for consensus aggregation.
4. **Cryptographic Privacy** — Federated Learning with **Secure Aggregation (SecAgg)** and
**Differential Privacy**. Only masked gradient updates leave the device.
See [`DESIGN.md`](./DESIGN.md) for the full architecture and [`docs/`](./docs) for the public site.
## Status
🌳 **All five milestones (M1–M5) landed.** `OrchardNode` runs a `NodeRuntime` actor that gates
work behind the opportunistic scheduler and does structured-output inference via Apple's
Foundation Models on OS 26+ (deterministic stub fallback elsewhere). `OrchardSwarm` is the
micro-swarm coordination layer — peer discovery, coordinator election, and a memory-aware
pipeline-parallel layer-shard planner. `OrchardRouter` is the global layer — job fragmentation,
redundant load-balanced assignment, and majority-vote consensus that outvotes and flags faulty
nodes. `OrchardCrypto` is the privacy layer — Bonawitz-style Secure Aggregation (Curve25519 +
exact pairwise mask cancellation) and differential privacy, recovering the federated mean from
masked vectors alone. `OrchardPilot` ties them together end-to-end on a real scientific workload.
The micro-swarm `ShardExecutor` seam is implemented too: a pure-Swift `PipelineRunner` runs a
`ShardPlan` as a distributed forward pass (bit-identical to monolithic), and `OrchardMLX` provides
a real Metal-accelerated executor on `mlx-swift` (opt-in; `just mlx-demo`). `OrchardTransport`
ships activations between pipeline stages over real Network.framework TCP, so `PipelineRunner`
drives a genuine multi-device pipeline (verified over localhost: distributed == monolithic), and
nodes find each other over the LAN via Bonjour auto-discovery (`just bonjour-test`). See the
[roadmap](./DESIGN.md#roadmap).
## Quick start
```bash
just setup # install toolchain + resolve packages
just build # build all targets
just test # run the test suite
just demo # run one task through a node (Foundation Models on OS 26+, else stub)
just pilot # run the full pipeline: distributed scan → consensus → federated refinement
just site # preview the GitHub Pages site locally
```
`just pilot` drives a scientific workload through all five layers at once:
```
🌳 Orchard pilot — distributed folding scan
scorer placement: solo (scorer fits on one device)
candidates: 12 evaluated across 4 nodes
consensus: 12/12 (dissents rejected: 9)
best conformation: [0.486, 0.336, 0.543] energy 0.4708
refined (federated DP gradient step):
[0.487, 0.208, 0.593] energy 0.3009
→ energy reduced by 0.1699 toward the native state [0.500, -0.300, 0.800]
```
`just demo` on an OS 26+ machine produces a real on-device structured summary:
```json
{
"title": "Apple Neural Engine Accelerates On-Device ML",
"summary": "The Apple Neural Engine enhances machine learning directly on devices…",
"topics": ["Apple", "Neural Engine", "On-Device ML", "Machine Learning"]
}
```
Requires macOS 26+ / Xcode 26+ for the on-device Foundation Models APIs. See [`TOOLS.md`](./TOOLS.md).
## Repository layout
```
orchard/
├── DESIGN.md # Architecture & system design
├── TOOLS.md # Toolchain, frameworks, dependencies
├── AGENTS.md # Guidance for AI coding agents
├── CLAUDE.md # Claude Code working agreement
├── justfile # Task runner
├── Package.swift # SwiftPM manifest
├── Sources/
│ ├── OrchardProtocol/ # Wire types, task specs, schemas, node capabilities
│ ├── OrchardNode/ # Node runtime, scheduler, Foundation Models adapter
│ ├── OrchardSwarm/ # Peer discovery, coordinator election, layer-shard planner
│ ├── OrchardRouter/ # Job fragmentation, redundant assignment, consensus aggregation
│ ├── OrchardCrypto/ # Secure Aggregation (SecAgg) + differential privacy
│ ├── OrchardPilot/ # Capstone: one scientific workload through every layer
│ ├── OrchardMLX/ # Metal-accelerated ShardExecutor on mlx-swift (opt-in)
│ ├── OrchardTransport/ # Cross-device transport over TCP + Bonjour auto-discovery
│ ├── orchard-demo/ # Node + swarm + router + crypto + transport demo executable
│ ├── orchard-pilot/ # End-to-end folding-scan pilot executable
│ └── orchard-mlx-demo/ # MLX sharded-execution demo (opt-in, Metal)
├── Tests/ # Swift Testing suites
├── docs/ # GitHub Pages site
└── .github/ # CI + Pages workflows
```
## License
[MIT](./LICENSE) — concept and scaffolding. Not affiliated with or endorsed by Apple Inc.