Syft.js is the “web” part of the OpenMined's open-source ecosystem for federated learning, which currently spans across web, iOS, Android, and servers/IoT.
Syft.js has following core features:
- 🛠️ Integration with PyGrid federated learning API.
- ⚙️ Training and inference of any PySyft model written in PyTorch or TensorFlow.
- 👤 Allows all data to stay on the user's device.
- 🔒 Support for secure multi-party computation and secure aggregation protocols using peer-to-peer WebRTC connections (in progress).
The library is built on top of TensorFlow.js.
There are a variety of additional privacy-preserving protections that may be applied, including differential privacy, muliti-party computation, and secure aggregation.
If you want to know how scalable federated systems are built, Towards Federated Learning at Scale is a fantastic introduction!
Note that syft.js needs Tensorflow.js library as peer dependency.
If you're using a package manager like NPM:
npm install --save @openmined/syft.js @tensorflow/tfjs-core
Or if Yarn is your cup of tea:
yarn add @openmined/syft.js @tensorflow/tfjs-core
If you're not using a package manager, you will be able to include Syft.js within a <script> tag.
In this case library classes will be available under syft global object.
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@openmined/syft.js@latest/dist/index.min.js"></script>
<script type="text/javascript">
// Create syft worker
const worker = syft.Syft({...});
...
</script>As a developer, there are few steps to building your own secure federated learning system upon the OpenMined infrastructure:
- 🤖 Develop ML model and training procedure (aka
Planin PySyft terminology) using PySyft. - 🌎 Host model and Plans on PyGrid, which will deal with all the federated learning components of your pipeline.
- 🎉 Execute the training on the variety of end-user devices using the client library (syft.js, SwiftSyft, KotlinSyft, PySyft).
- 🔒 Securely aggregate trained user models in PyGrid.
📓 The entire workflow and process is described in greater detail in the Web & Mobile Federated Learning project roadmap.
Syft.js provides minimalistic API to communicate with federated learning PyGrid endpoints and execute PySyft's Plans in a browser. The federated learning cycle implemented with syft.js would contain following steps:
- Register into training cycle on PyGrid.
- Download required model and Plans from PyGrid.
- Execute the Plan with given model parameters and local user's data (multiple times) to create better model.
- Submit difference between original and trained model parameters for aggregation.
These steps can be expressed in the following code:
import * as tf from '@tensorflow/tfjs-core';
import { Syft } from '@openmined/syft.js';
const gridUrl = 'ws://pygrid.myserver.com:5000';
const modelName = 'my-model';
const modelVersion = '1.0.0';
// if the model is protected with authentication token (optional)
const authToken = '...';
const worker = new Syft({ gridUrl, verbose: true });
const job = await worker.newJob({ modelName, modelVersion, authToken });
job.request();
job.on('accepted', async ({ model, clientConfig }) => {
const batchSize = clientConfig.batch_size;
const lr = clientConfig.lr;
// Load data.
const [data, target] = LOAD_DATA();
const batches = MAKE_BATCHES(data, target, batchSize);
// Load model parameters.
let modelParams = model.params.map((p) => p.clone());
// Main training loop.
for (let [dataBatch, targetBatch] of batches) {
// NOTE: this is just one possible example.
// Plan name (e.g. 'training_plan'), its input arguments and outputs depends on FL configuration and actual Plan implementation.
let updatedModelParams = await job.plans['training_plan'].execute(
job.worker,
dataBatch,
targetBatch,
batchSize,
lr,
...modelParams
);
// Use updated model params in the next iteration.
for (let i = 0; i < modelParams.length; i++) {
modelParams[i].dispose();
modelParams[i] = updatedModelParams[i];
}
}
// Calculate & send model diff.
const modelDiff = await model.createSerializedDiff(modelParams);
await job.report(modelDiff);
});
job.on('rejected', ({ timeout }) => {
// Handle the job rejection, e.g. re-try after timeout.
});
job.on('error', (err) => {
// Handle errors.
});The Plan execution and Model training can be implemented easier
using training helper that will do training loop for you
(model, batch size, etc. are automatically taken from Job):
// Main training loop.
const training = job.train('training_plan', {
inputs: [/* ... */],
outputs: [/* ... */],
data,
target,
});
training.on('end', async () => {
// Calculate & send model diff.
const modelDiff = await model.createSerializedDiff(modelParams);
await job.report(modelDiff);
});inputs and outputs need to be specified using PlanInputSpec and PlanOutputSpec
and need to match with Plan's arguments and outputs.
For example, if the Plan has following arguments and outputs:
loss, accuracy, modelParams1, modelParams2, modelParams3, modelParams4 =
plan(dataBatch, targetBatch, batchSize, lr, modelParams1, modelParams2, modelParams3, modelParams4)
Corresponding inputs, outputs in job.train will be:
const inputs = [
new PlanInputSpec(PlanInputSpec.TYPE_DATA),
new PlanInputSpec(PlanInputSpec.TYPE_TARGET),
new PlanInputSpec(PlanInputSpec.TYPE_BATCH_SIZE),
new PlanInputSpec(PlanInputSpec.TYPE_CLIENT_CONFIG_PARAM, 'lr'),
new PlanInputSpec(PlanInputSpec.TYPE_MODEL_PARAM, 'param1', 0),
new PlanInputSpec(PlanInputSpec.TYPE_MODEL_PARAM, 'param2', 1),
new PlanInputSpec(PlanInputSpec.TYPE_MODEL_PARAM, 'param3', 2),
new PlanInputSpec(PlanInputSpec.TYPE_MODEL_PARAM, 'param4', 3),
];
const outputs = [
new PlanOutputSpec(PlanOutputSpec.TYPE_LOSS),
new PlanOutputSpec(PlanOutputSpec.TYPE_METRIC, 'accuracy'),
new PlanOutputSpec(PlanOutputSpec.TYPE_MODEL_PARAM, 'param1', 0),
new PlanOutputSpec(PlanOutputSpec.TYPE_MODEL_PARAM, 'param2', 1),
new PlanOutputSpec(PlanOutputSpec.TYPE_MODEL_PARAM, 'param3', 2),
new PlanOutputSpec(PlanOutputSpec.TYPE_MODEL_PARAM, 'param4', 3),
];PlanTrainer allows stopping and resuming the training using stop and resume methods:
// Main training loop.
const training = job.train('training_plan', {
inputs: [/* ... */],
outputs: [/* ... */],
data,
target,
});
training.on('start', () => {
// training is started!
});
training.on('stop', () => {
// training is stopped!
});
document.getElementById('stop-button').onclick = () => {
training.stop();
};
document.getElementById('resume-button').onclick = () => {
training.resume();
};stop method returns current training state as PlanTrainerCheckpoint object,
which can be serialized to JSON to restored from JSON later to continue the training:
const checkpoint = await training.stop();
const checkpointJson = await checkpoint.toJSON();
const checkpointJsonString = JSON.stringify(checkpointJson);
localStorage.setItem('checkpoint', checkpointJsonString);
// ... checkpoint can survive page reload ...
const checkpointJsonString = localStorage.getItem('checkpoint');
const checkpointJson = JSON.parse(checkpointJsonString);
const checkpoint = PlanTrainerCheckpoint.fromJSON(worker, checkpointJson);
// Main training loop.
const training = job.train('training_plan', {
// Pass checkpoint into train method to resume from it
// NOTE: checkpoint doesn't store Plan and training data, these still need to be supplied
checkpoint,
inputs: [/* ... */],
outputs: [/* ... */],
data,
target,
});Checkpoint can be created directly from PlanTrainer object
using createCheckpoint method and applied back using applyCheckpoint:
const checkpoint = training.createCheckpoint();
// ...
training.applyCheckpoint(checkpoint);
training.resume();One way to provide training data into PlanTrainer is
to prepare and pass data and target parameters as plain tf.Tensor's.
Another way is to use Dataset and DataLoader classes,
which are simplified version of PyTorch's implementation.
Dataset class needs to be extended to implement element-wise access to the data.
Resulting dataset is used with DataLoader that handles shuffling and batching of dataset elements.
The DataLoader can passed as data parameter into the PlanTrainer.
class MyDataset extends data.Dataset {
constructor() {
super();
// this.data = ...;
// this.target = ...;
}
getItem(index) {
return [
this.data[index],
this.target[index]
];
}
get length() {
return this.data.length;
}
}
const dataset = new MyDataset();
const dl = new DataLoader({dataset, batchSize: 64, shuffle: true});
// Use with PlanTrainer
const training = job.train('training_plan', {
inputs: [/* ... */],
outputs: [/* ... */],
data: dl
});
// Or use with custom training loop
for (const [data, target] of dl) {
// ...
}MNIST example has implementation of MNIST dataset based on Dataset class
and DataLoader usage and additionally introduces data transformations using Transform.
See API Documentation for complete reference.
The “Hello World” syft.js demo is MNIST training example located in examples/mnist folder.
It demonstrates how a simple neural net model created in PySyft can be trained in a browser
and the result of training averaged from multiple federated learning participants.
Running the demo is multi-stage and multi-component process (as the federated learning itself).
Below are example instructions that assume you
want to put everything under ~/fl-demo folder.
It is recommended that you install python packages in separate virtualenv or conda environment, e.g.:
virtualenv -p python3 syft
source syft/bin/activateor
conda create -n syft python=3.7
conda activate syftNow, you will need to install following packages:
-
PySyft. Follow PySyft installation guide to install the latest 0.2.x branch of PySyft.
-
PyGrid. Follow PyGrid documentation to install the latest
devbranch of PyGrid. -
Syft.js with MNIST demo. Check out the latest
devbranch of syft.js with MNIST demo app included:cd ~/fl-demo git clone https://github.com/OpenMined/syft.js cd syft.js npm install cd examples/mnist npm install
Syft.js connects to PyGrid to pick up the model and training Plan. For the demo to work, we need to populate that data into PyGrid.
See Getting Started for details. It is possible to start PyGrid Node using docker or using console script.
We assume you don't need to change default PyGrid Node configuration and it listens
on the localhost:5000. If you need to use different host/port,
PyGrid URL will need to be adjusted accordingly in further steps.
After PyGrid is running, the next step is to create the model and training plan and host them in PyGrid. MNIST example jupyter notebooks guide you through this process.
Fire up jupyter notebook in PyGrid root folder:
cd ~/fl-demo/PyGrid
jupyter notebook --notebook-dir=$(pwd)In the console, you should see URL you should open, or the browser will open automatically. After this, navigate to examples/model-centric and run the first notebook. At this point, you can pull down the model and training plan with syft.js. However, if you'd like to see how to execute the plan using the PySyft FL worker, try running the second notebook.
In case you need to reset PyGrid Node database to blank state, stop the process with Ctrl+C and remove databaseGateway.db file in PyGrid.
Or, if you used docker-compose, stop and re-start it using docker-compose up --force-recreate command.
Finally, we got to the browser part of the demo:
cd ~/fl-demo/syft.js/examples/mnist
npm startThis should start development server and open localhost:8080 in the browser.
Assuming PyGrid URL, MNIST model name and version were not modified in previous steps, just
press “Start FL Worker”.
You should see following in dev console:
- Syft.js registers into training cycle on PyGrid and gets configuration, Plan, and the model.
- App loads MNIST dataset and executes the training plan with each data batch. Charts are updated during this process, and you should see the training loss going down and the accuracy going up.
- After the training is complete, model diff is submitted to PyGrid.
If “Keep making cycle requests” is checked, the whole cycle process is repeated until PyGrid tells worker that model training is complete.
Syft.js has been tested with PySyft 0.2.7
Syft.js has been tested with the latest version of PyGrid on master.
Syft.js was tested with Tensorflow.js v1.2.5.
Syft.js was tested with Chrome and Firefox browsers.
For support in using this library, please join the #lib_syftjs Slack channel. If you’d like to follow along with any code changes to the library, please join the #code_syftjs Slack channel. Click here to join our Slack community!
Please check open issues as a starting point.
Bug reports and feature suggestions are welcomed as well.
The workflow is usual for github, the master branch is considered stable and the dev branch is actively under development:
- Star, fork, and clone the
syft.jsrepository. - Create a new branch for changes from
dev. - Push changes to this branch.
- Submit a PR to OpenMined/syft.js.
- PR is reviewed and accepted.
Read the contribution guide as a good starting place.
Additionally, we welcome you to the slack for queries related to the library and contribution in general.
The Slack channel #lib_syftjs is specific to syft.js development. See you there!
These people were integral part of the efforts to bring syft.js to fruition and in its active development.
 Patrick Cason 🤔 💻 🎨 📖 💼 |
 Vova Manannikov 💻 📖 |
 Mike Nolan 💻 |
 Ravikant Singh 💻 |
 varun khare 💻 |
 Pedro Espíndula 📖 |
 José Benardi de Souza Nunes |
 Tajinder Singh 💻 |