zk0: Federated Learning for Robotics AI

Open-source federated learning platform for SmolVLA models on SO-100 datasets using Flower framework.

Latest Model Release

• Model: ivelin/zk0-smolvla-fl
• Training: 250 rounds FedProx (μ=0.01), dynamic LR/MU scheduling
• Final Policy Loss: 0.495
• Clients: 4 clients on diverse SO-100 tasks
• Framework: Flower + SmolVLA + SO-100 datasets

from transformers import AutoModel
model = AutoModel.from_pretrained("ivelin/zk0-smolvla-fl")

Quick Start

For detailed setup, see docs/INSTALLATION.

Prerequisites

• Python 3.10+, Conda, Git.
• NVIDIA GPU recommended.

Clone and Setup

git clone https://github.com/ivelin/zk0.git
cd zk0

# Create conda env
conda create -n zk0 python=3.10 -y
conda activate zk0
conda install ffmpeg=7.1.1 -c conda-forge

# Install deps
pip install -e .

# Env vars
cp .env.example .env  # Edit as needed (e.g., HF_TOKEN)

Run the Simulation

See docs/INSTALLATION for full instructions.

# Tiny test run (1 round)
./train-fl-simulation.sh --tiny

Production Deployment

For production use with multiple distributed nodes, use the zk0bot CLI tool for orchestrated federated learning:

# Server operator: Start the central coordinator
zk0bot server start

# Client operators (each with their private dataset)
zk0bot client start hf:yourusername/your-private-dataset
# or
zk0bot client start local:/path/to/your/dataset

# Monitor status
zk0bot status

# Stop services
zk0bot server stop
zk0bot client stop

The CLI uses Flower's Deployment Engine (SuperLink, SuperNodes, SuperExec) for stateless, insecure-mode operation. Server runs continuously, automatically managing FL sessions based on connected clients. See docs/NODE-OPERATORS.md for detailed setup and security notes.

For run details, outputs, experiment tracking, and model pushing, see docs/RUNNING. For repository branches and contributing guidelines, see CONTRIBUTING.

Project Status

Current Stage: Beta

The project is ready for local simulation testing with multiple clients and datasets.

In Progress

• Preparing client and server modules for production deployment
• ZK proofs, onchain coordination.

Config: 12 clients available (4 active); 500 rounds; policy loss metric; FedProx (μ=0.01); server-side evaluation.

For full details, see docs/ARCHITECTURE.

Contributing

We welcome contributions from the community! At this Beta stage, we're particularly interested in:

Node Operators

Requirements

• Hardware: LeRobot SO100 or SO101 robotic arm. Contributors can either:
  • Build a DIY arm using the official LeRobot SO101 repository
  • Or order a pre-built kit, for example this one from Florin who runs the Austin Robotics Meetup.
• Compute: Local machine with RTX 3090 GPU or better, compatible with LeRobot library
• Network: Stable internet connection for federated communication
• Data: Unique training data from your robotics setup

If you meet these requirements, we'd love for you to join as a node operator. Your unique training data and compute resources will help improve the federated learning system. For detailed setup instructions, see CONTRIBUTING.

Other Ways to Contribute

There are several ways you can contribute to this project:

1. Node Operators: Join the federated network with your hardware and data
2. Code Contributors: Improve the codebase, add features, fix bugs
3. Documentation: Help improve documentation and tutorials
4. Testing: Report bugs, test new features, improve test coverage
5. Feedback: Share your experience and suggestions

For more details on each, see CONTRIBUTING.

Test change for xAI delta sync

Test change for xAI delta sync

Test change for xAI delta sync

Test change for xAI delta sync

Test change for xAI delta sync