BranchKey Python Client

BK_logo

Official Python client for the BranchKey federated learning and analytics platform. Upload model weights, compute and upload federated analytics, download aggregated results, and track training runs — without raw data leaving your site.

Full documentation: app.branchkey.com/docs. This page is the short version.

3.0.0 is a breaking release. The AMQP/RabbitMQ transport is gone; WebSocket is the only transport and needs no configuration. Drop any rabbitmq_config or use_websocket argument — both now raise TypeError. Nothing else changed. pip install "branchkey<3" pins the previous behaviour.

Installation

pip install branchkey

Requires Python 3.10 or higher (tested on 3.10 – 3.14).

Quick start

Create a leaf entity on the platform to obtain its credentials — see Getting started → Access and setup.

from branchkey import Client, Credentials

client = Client(
    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",
    )
)

# 1. Upload this round's model weights.
#    `weighting` is this site's influence during aggregation — usually the
#    number of training samples. `parameters` is a list of numpy arrays.
file_path = client.save_weights("model_weights", weighting=1000, parameters=parameters)
file_id = client.file_upload(file_path)

# 2. Wait for the aggregated result and download it.
aggregation_id = client.queue.get(block=True)
client.file_download(aggregation_id)  # -> {client.output_dir}/{aggregation_id}.npz

client.queue.get(block=False) polls instead of blocking, and client.get_latest_aggregation_id() returns the most recent notification or None.

The downloaded archive contains the aggregated layers only, under the keys layer_0, layer_1, …, with no weighting field:

import numpy as np

npz = np.load(f"{client.output_dir}/{aggregation_id}.npz")
parameters = [npz[k] for k in sorted(npz.files) if k.startswith("layer_")]

Federated analytics

Federated analytics answers a question about the data itself — "what is the mean age across the federation?" — with no model trained. Hand save_analytics a raw column and the SDK reduces it to six combinable numbers before anything is written to disk or sent over the wire:

file_path = client.save_analytics(
    {
        "age": patients["age"].to_numpy(),
        "tumour_volume": patients["volume"].to_numpy(),
    }
)
file_id = client.file_upload(file_path)

The archive holds six entries per column, and nothing else:

age -> age_n, age_sum, age_sumsq, age_min, age_max, age_nan

n is the count, sum is Σx, sumsq is Σx², min/max are the extremes, and nan is how many values were missing and left out. Not a single patient's age is in the file. That is a property of the library, not a request made of you: there is no argument that turns it off.

Sums are sent rather than statistics because sums combine across sites and statistics do not — two sites each reporting a variance of zero can pool to a variance of 1600. From the bundle the platform derives count, sum, min, max, range, mean, variance and standard deviation, all exact. Columns must be 1-D and numeric; NaN is dropped by default and counted in _nan.

Already computed the number yourself — a preprocessing planner's output, a label histogram? Send it with save_fields instead, and choose its combining operation in the branch configuration.

Where to go next

Topic	Link
Getting access and your first leaf	Access and setup
Federated analytics in full, with worked examples	Concepts → Federated analytics
`save_fields`, per-field combining operations	Per-field aggregation
Branch settings, aggregation strategy, run control	Configuring a branch
Client configuration, retries, proxies, framework examples	Documentation home
Common questions and troubleshooting	FAQ

Support

Documentation: app.branchkey.com/docs
Website: branchkey.com
Email: info@branchkey.com

Licence

GPL-3.0 — the full text ships with the package as LICENSE.

BranchKey — Federated Learning Platform

Release files for branchkey 3.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for branchkey 3.0.1
File	Size	Uploaded
branchkey-3.0.1.tar.gz	32.1 kB	Sep 22, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for branchkey 3.0.1
File	Interpreter	ABI	Platform	Reset
branchkey-3.0.1-py3-none-any.whl 32.1 kB Sep 22, 2026	Python 3	none	any	Details

Total release size: 64.1 kB

Release files / branchkey-3.0.1.tar.gz

Download URL	branchkey-3.0.1.tar.gz
Size	32.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`883662eb1fe3a5c4ff74c7479d002fca3c39e23bbbc8e859ef6d12dc8cfadb76`
BLAKE2b-256 checksum How to use checksums	`340688d79b8e33143a3c801953dca5a602bdf908f74b8b5f7a29dd8d801c4375`
Upload date	Sep 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.7`

Release files / branchkey-3.0.1-py3-none-any.whl

Download URL	branchkey-3.0.1-py3-none-any.whl
Size	32.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`ee72e4e98d419edaa6c54329a66e2b24ac43c34ea7261832aabed06de967cad9`
BLAKE2b-256 checksum How to use checksums	`e566df0978388c0fe24af9f392d010ffe08bad42782b5486ccff30704de2837f`
Upload date	Sep 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.7`