FLSim a flexible, standalone library written in PyTorch that simulates FL settings with a minimal, easy-to-use API

Last update: Jan 02, 2023

Related tags

Overview

Federated Learning Simulator (FLSim)

Federated Learning Simulator (FLSim) is a flexible, standalone library written in PyTorch that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as computer vision and natural text. Currently FLSim supports cross-device FL, where millions of clients' devices (e.g. phones) traing a model collaboratively together.

FLSim is scalable and fast. It supports differential privacy (DP), secure aggregation (secAgg), and variety of compression techniques.

In FL, a model is trained collaboratively by multiple clients that each have their own local data, and a central server moderates training, e.g. by aggregating model updates from multiple clients.

In FLSim, developers only need to define a dataset, model, and metrics reporter. All other aspects of FL training are handled internally by the FLSim core library.

FLSim

Library Structure

FLSim core components follow the same semantic as FedAvg. The server comprises three main features: selector, aggregator, and optimizer at a high level. The selector selects clients for training, and the aggregate aggregates client updates until a round is complete. Then, the optimizer optimizes the server model based on the aggregated gradients. The server communicates with the clients via the channel. The channel then compresses the message between the server and the clients. Locally, the client composes of a dataset and a local optimizer. This local optimizer can be SGD, FedProx, or a custom Pytorch optimizer.

Installation

The latest release of FLSim can be installed via pip:

pip install flsim

You can also install directly from the source for the latest features (along with its quirks and potentially ocassional bugs):

git clone https://github.com/facebookresearch/FLSim.git
cd FLSim
pip install -e .

Getting started

To implement a central training loop in the FL setting using FLSim, a developer simply performs the following steps:

Build their own data pipeline to assign individual rows of training data to client devices (to simulate data is distributed across client devices)
Create a corresponding nn/Module model and wrap it in an FL model.
Define a custom metrics reporter that computes and collects metrics of interest (e.g., accuracy) throughout training.
Set the desired hyperparameters in a config.

Usage Example

Tutorials

To see the details, please refer to the tutorials that we have prepared.

Examples

We have prepared the runnable exampels for 2 of the tutorials above:

Contributing

See the CONTRIBUTING for how to contribute to this library.

License

This code is released under Apache 2.0, as found in the LICENSE file.

Comments

Bug Fix#36: fix imports in tests.
Types of changes

[x ] Bug fix (non-breaking change which fixes an issue)

[ ] New feature (non-breaking change which adds functionality)

[ ] Breaking change (fix or feature that would cause existing functionality to change)

[ ] Docs change / refactoring / dependency upgrade

Motivation and Context / Related issue

Bug Fix#36: fix imports in tests.

How Has This Been Tested (if it applies)

pytest -ra is able to discover all tests now.

Checklist

[x] The documentation is up-to-date with the changes I made.

[x] I have read the CONTRIBUTING document and completed the CLA (see CONTRIBUTING).

[x ] All tests passed, and additional code has been covered with new tests.

CLA Signed
opened by ghaccount 8
Vr
Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)

[ ] New feature (non-breaking change which adds functionality)

[ ] Breaking change (fix or feature that would cause existing functionality to change)

[ ] Docs change / refactoring / dependency upgrade

Motivation and Context / Related issue

How Has This Been Tested (if it applies)

Checklist

[ ] The documentation is up-to-date with the changes I made.

[ ] I have read the CONTRIBUTING document and completed the CLA (see CONTRIBUTING).

[ ] All tests passed, and additional code has been covered with new tests.

CLA Signed
opened by JohnlNguyen 6
Move optimizer_test_utils to optimizers directory

Summary: it is currently located at the top-level tests directory. However the top-level tests directory does not really make sense as each component is organized into its dedicated directory. optimizer_test_utils.py belongs to the optimizer directory in that sense. In this diff, we move the file to the optimizer directory and fixes the reference.

Differential Revision: D32241821
CLA Signed fb-exported Merged

opened by jessemin 3
Does the backend handle Federated learning asynchronously?

I found this repo from this blog: - https://ai.facebook.com/blog/asynchronous-federated-learning/ However I do not find any mentioning on this repo and also I cannot decipher from the code examples whether this is synchronous version or asynchronous version of Federated learning? Can you please clarify this for me? And also if this is the asynchronous version how can I dive deeper in to the libraries and look at the code of implementation for the asynch handling mechanism?

Thank you

opened by 111Kaushal 2
Remove test_pytorch_local_dataset_factory

Summary: This test had been very flaky about 1+ year ago an d never been revived since then. Deleting it from the codebase.

Differential Revision: D32415979
CLA Signed fb-exported Merged

opened by jessemin 2
FedSGD with virtual batching

🚀 Feature

Motivation

Create a memory efficient client to run FedSGD. If a client has many examples, running FedSGD (taking the gradient of the model based on all of the client's data) can lead to OOM. In this PR, we fix this problem by still calling optimizer.step once at the end of local training to simulate the effect of FedSGD.>

opened by JohnlNguyen 0
Add Fednova as a benchmark

Summary:

What?

Adding FedNova as a benchmark

Why?

FedNova is a well known paper that fixes the objective inconsistency problem

Differential Revision: D34668291
CLA Signed fb-exported

opened by JohnlNguyen 1

Having to `import flsim.configs` before creating config from json is unintuitive

🚀 Feature

This code works

import flsim.configs <-- 
from flsim.utils.config_utils import fl_config_from_json

json_config = {
    "trainer": {
    }
}
cfg = fl_config_from_json(json_config)

This code doesn't work

from flsim.utils.config_utils import fl_config_from_json

json_config = {
    "trainer": {
    }
}
cfg = fl_config_from_json(json_config)

Motivation

Having to import flsim.configs is unintuitive and not clear from the user perspective

Pitch

Alternatives

Additional context

opened by JohnlNguyen 0

Fix sent140 example

Summary:

What?

Fix tutorial to word embedding to resolve the poor accuracy problem

Why?

https://github.com/facebookresearch/FLSim/issues/34

Differential Revision: D34149392
CLA Signed fb-exported

opened by JohnlNguyen 1
low test accuracy in Sentiment classification with LEAF's Sent140 tutorial?

❓ Questions and Help

Until we move the questions to another medium, feel free to use this as your question:

Question

I tried this tutorial https://github.com/facebookresearch/FLSim/blob/main/tutorials/sent140_tutorial.ipynb And accuracy is less that random guess (50%)!

Any suggestions or approaches to improve accuracy for this tutorial?

from tutorial: Running (epoch = 1, round = 1, global round = 1) for Test (epoch = 1, round = 1, global round = 1), Loss/Test: 0.8683878255035598 (epoch = 1, round = 1, global round = 1), Accuracy/Test: 49.61439588688946 {'Accuracy': 49.61439588688946}

opened by ghaccount 0

Releases(v0.1.0)

v0.1.0(Jul 27, 2022)

Source code(tar.gz)
Source code(zip)
v0.0.2(Jul 26, 2022)

Source code(tar.gz)
Source code(zip)
v0.0.1(Dec 9, 2021)

We are excited to announce the release of FLSim 0.0.1.

Introduction

How does one train a machine learning model without access to user data? Federated Learning (FL) is the technology that answers this question. In a nutshell, FL is a way for many users to learn a machine learning model without sharing data collaboratively. The two scenarios for FL, cross-silo and cross-device. Cross-silo provides technologies for collaborative learning between a few large organizations with massive silo datasets. Cross-device provides collaborative learning between many small user devices with small local datasets. Cross-device FL, where millions or even billions of users cooperate on learning a model, is a much more complex problem and attracted less attention from the research community. We designed FLSim to address the cross-device FL use case.

Federated Learning at Scale

Large-scale cross-device Federated Learning (FL) is a federated learning paradigm with several challenges that differentiate it from cross-silo FL: millions of clients coordinating with a central server and training instability due to the significant cohort problem. With these challenges in mind, we built FLSim to be scalable while easy to use, and FLSim can scale to thousands of clients per round using only 1 GPU. We hope FLSim will equip researchers to tackle problems with federated learning at scale.

FLSim

Library Structure

FLSim core components follow the same semantic as FedAvg. The server comprises three main features: selector, aggregator, and optimizer at a high level. The selector selects clients for training, and the aggregate aggregates client updates until a round is complete. Then, the optimizer optimizes the server model based on the aggregated gradients. The server communicates with the clients via the channel. The channel then compresses the message between the server and the clients. Locally, the client composes of a dataset and a local optimizer. This local optimizer can be SGD, FedProx, or a custom Pytorch optimizer.

Included Datasets

Currently, FLSim supports all datasets from LEAF including FEMNIST, Shakespeare, Sent140, CelebA, Synthetic and Reddit. Additionally, we support MNIST and CIFAR-10.

Included Algorithms

FLSim supports standard FedAvg, and other federated learning methods such as FedAdam, FedProx, FedAvgM, FedBuff, FedLARS, and FedLAMB.

What’s next?

We hope FLSim will foster large-scale cross-device FL research. Soon, we plan to add support for personalization in early 2022. Throughout 2022, we plan to gather feedback and improve usability. We plan to continue to grow our collection of algorithms, datasets, and models.
Source code(tar.gz)
Source code(zip)

Owner

Meta Research

GitHub Repository

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FLSim a flexible, standalone library written in PyTorch that simulates FL settings with a minimal, easy-to-use API

Related tags

Overview

Federated Learning Simulator (FLSim)

FLSim

Library Structure

Installation

Getting started

Usage Example

Tutorials

Examples

Contributing

License

Comments

Types of changes

Motivation and Context / Related issue

How Has This Been Tested (if it applies)

Checklist

Types of changes

Motivation and Context / Related issue

How Has This Been Tested (if it applies)

Checklist

🚀 Feature

Motivation

What?

Why?

🚀 Feature

Motivation

Pitch

Alternatives

Additional context

What?

Why?

❓ Questions and Help

Question

Releases(v0.1.0)

v0.1.0(Jul 27, 2022)

v0.0.2(Jul 26, 2022)

v0.0.1(Dec 9, 2021)

Introduction

Federated Learning at Scale

FLSim

Library Structure

Included Datasets

Included Algorithms

What’s next?

Owner

Meta Research

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

Pytorch version of VidLanKD: Improving Language Understanding viaVideo-Distilled Knowledge Transfer

The project is associated with the recently-launched ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) to provide participants with baseline systems for speech recognition and speaker diarization in conference scenario.

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