branchkey

BranchKey Python Client

Official Python client for the BranchKey federated learning platform. This library provides a simple interface to upload model weights, download aggregated results, and track training runs.

Installation

pip install branchkey

Requirements: Python 3.9 or higher

Quick Start

1. Get Credentials

Create a leaf entity through the BranchKey platform to obtain credentials via the /v2/entities API endpoint:

credentials = {
    "id": "your-leaf-uuid",
    "name": "my-client",
    "session_token": "your-session-token-uuid",
    "owner_id": "your-user-uuid",
    "tree_id": "your-tree-uuid",
    "branch_id": "your-branch-uuid"
}

2. Initialize Client

from branchkey.client import Client

# Connect to BranchKey
client = Client(credentials, host="https://app.branchkey.com")

3. Upload Model Weights

import numpy as np

# Prepare model weights
weighting = 1000  # Weight for aggregation (typically number of samples)
parameters = [layer1_weights, layer2_weights, ...]

# Save and upload
file_path = client.save_weights("model_weights", weighting, parameters)
file_id = client.file_upload(file_path)
print(f"Uploaded: {file_id}")

4. Download Aggregated Results

# Check for aggregation notifications
if not client.queue.empty():
    aggregation_id = client.queue.get(block=False)
    client.file_download(aggregation_id)
    # Downloaded to: ./aggregated_files/{aggregation_id}.npz

Configuration Options

client = Client(
    credentials,
    host="https://app.branchkey.com",  # API endpoint
    rbmq_host=None,                     # RabbitMQ host (auto-derived from host)
    rbmq_port=5672,                     # RabbitMQ port
    ssl=True,                           # Verify SSL certificates
    wait_for_run=False,                 # Wait if run is paused
    run_check_interval_s=30,            # Run status check interval
    proxies=None                        # HTTP/HTTPS proxy dict
)

Model Weight Format

Model weights are stored in compressed NPZ format.

Structure

# Format: (weighting, [list_of_parameter_arrays])
weighting = 1000  # Weight for aggregation (see below)
parameters = [layer1, layer2, ...]  # List of numpy arrays

Weighting Options

The weighting parameter controls how much influence this update has during aggregation:

1. By Sample Count (Most Common)

weighting = len(train_dataset)  # e.g., 1000 samples
# Client with 1000 samples has 2x influence of client with 500 samples

2. Equal Weighting

weighting = 1  # All clients have equal influence

3. Quality-Based Weighting

validation_accuracy = 0.85
weighting = len(train_dataset) * validation_accuracy  # Weight by quality

4. Manual Weighting

weighting = 5.0  # Trusted client gets higher weight

PyTorch Example

import numpy as np

# Method 1: Using client helper (recommended)
weighting = len(train_dataset)
parameters = []
for name, param in model.named_parameters():
    parameters.append(param.data.cpu().detach().numpy())

file_path = client.save_weights("model_weights", weighting, parameters)
file_id = client.file_upload(file_path)

# Method 2: Using convert_pytorch_numpy
weighting, parameters = client.convert_pytorch_numpy(
    model.named_parameters(),
    weighting=len(train_dataset)
)
file_path = client.save_weights("model_weights", weighting, parameters)
file_id = client.file_upload(file_path)

TensorFlow/Keras Example

import numpy as np

weighting = len(train_dataset)
parameters = [layer.numpy() for layer in model.trainable_weights]

file_path = client.save_weights("model_weights", weighting, parameters)
file_id = client.file_upload(file_path)

Manual NPZ Creation

import numpy as np

# Save manually (without using client helper)
arrays_dict = {'weighting': np.array([weighting], dtype=np.float64)}
for i, arr in enumerate(parameters):
    arrays_dict[f'layer_{i}'] = arr

np.savez_compressed("model_weights.npz", **arrays_dict)  # Must include .npz

Loading Aggregated Weights

# Load aggregated weights from NPZ file
npz_data = np.load("aggregated_files/aggregation_id.npz")

# Note: Aggregated results only contain layers (no weighting)
layer_keys = sorted([k for k in npz_data.files if k.startswith('layer_')])
parameters = [npz_data[k] for k in layer_keys]

# Apply to your model
for i, param in enumerate(model.parameters()):
    param.data = torch.from_numpy(parameters[i])

Example NPZ File Contents

Client Upload Format (with weighting):

>>> npz_data.files
['weighting', 'layer_0', 'layer_1', 'layer_2', 'layer_3', ...]

>>> npz_data['weighting']
array([1530.])  # Weight for aggregation

>>> npz_data['layer_0'].shape, npz_data['layer_0'].dtype
((32, 1, 5, 5), dtype('float32'))

>>> npz_data['layer_0'][:1, :2, :2, :]
array([[[[-0.18576819, -0.03041792,  0.19532707, -0.11234483, -0.01512307],
         [ 0.19993757, -0.06492048,  0.08324468, -0.19899307, -0.0412709 ]]]],
       dtype=float32)

Aggregated Result Format (layers only):

>>> npz_data.files
['layer_0', 'layer_1', 'layer_2', ...]  # No weighting in aggregated results

Performance Metrics

Submit training or testing metrics:

import json

metrics = {"accuracy": 0.95, "loss": 0.12}
client.send_performance_metrics(
    aggregation_id="aggregation-uuid",
    data=json.dumps(metrics),
    mode="test"  # "test", "train", or "non-federated"
)

Client Properties

client.run_status        # Current run status: "start", "stop", or "pause"
client.run_number        # Current run iteration
client.leaf_id           # Your leaf UUID
client.branch_id         # Parent branch UUID
client.is_authenticated  # Authentication status

Proxy Support

For networks requiring proxy access:

proxies = {
    'http': 'http://user:[e-mail]:8080',
    'https': 'http://user:[e-mail]:8080',
}
client = Client(credentials, host="https://app.branchkey.com", proxies=proxies)

Development

Running Tests

# Clone repository
git clone https://gitlab.com/branchkey/client_application.git
cd client_application

# Run tests with Docker (requires Docker)
make local-test

# Or manually
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m unittest -v

Support

• Website: https://branchkey.com
• Repository: https://gitlab.com/branchkey/client_application
• Email: [e-mail]

BranchKey - Federated Learning Platform