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

Related tags

Deep LearningFLSim
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:

  1. Build their own data pipeline to assign individual rows of training data to client devices (to simulate data is distributed across client devices)
  2. Create a corresponding nn/Module model and wrap it in an FL model.
  3. Define a custom metrics reporter that computes and collects metrics of interest (e.g., accuracy) throughout training.
  4. 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.

    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

    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

    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?

    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

    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

    FedSGD with virtual batching

    πŸš€ 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

    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

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

    πŸš€ 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

    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?

    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)

  • v0.0.2(Jul 26, 2022)

  • 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
Meta Research
GitHub Repository
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
[SIGGRAPH 2022 Journal Track] AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars Fangzhou Hong1*  Mingyuan Zhang1*  Liang Pan1  Zhongang Cai1,2,3  Lei Yang2 

Fangzhou Hong 749 Jan 04, 2023
My solution for the 7th place / 245 in the Umoja Hack 2022 challenge

Umoja Hack 2022 : Insurance Claim Challenge My solution for the 7th place / 245 in the Umoja Hack 2022 challenge Umoja Hack Africa is a yearly hackath

Souames Annis 17 Jun 03, 2022
4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR) at PBVS2022

A Two-Stage Shake-Shake Network for Long-tailed Recognition of SAR Aerial View Objects 4st place solution for the PBVS 2022 Multi-modal Aerial View Ob

LinpengPan 5 Nov 09, 2022
ν†΅μΌλœ DataScience 폴더 ꡬ쑰 제곡 및 κ°€μƒν™˜κ²½ μž‘μ—…μ˜ 뢀담감 ν•΄μ†Œ

Lucas coded by linux shell λͺ©μ°¨ Mac버전 CookieCutter (autoenv) 1.How to Install autoenv 2.폴더 μ§„μž… μ‹œ, activate κ΅¬ν˜„ν•˜κΈ° 3.폴더 νƒˆμΆœ μ‹œ, deactivate κ΅¬ν˜„ν•˜κΈ° 4.Alias μ„€μ •ν•˜κΈ° 5

ello 3 Feb 21, 2022
A Lightweight Experiment & Resource Monitoring Tool πŸ“Ί

Lightweight Experiment & Resource Monitoring πŸ“Ί "Did I already run this experiment before? How many resources are currently available on my cluster?"

170 Dec 28, 2022
Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

Dual Self-Attention Network for Multivariate Time Series Forecasting 20.10.26 Update: Due to the difficulty of installation and code maintenance cause

Kyon Huang 223 Dec 16, 2022
Public repo for the ICCV2021-CVAMD paper "Is it Time to Replace CNNs with Transformers for Medical Images?"

Is it Time to Replace CNNs with Transformers for Medical Images? Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (C

Christos Matsoukas 80 Dec 27, 2022
Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

OFA Sys 1.4k Jan 08, 2023
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
A practical ML pipeline for data labeling with experiment tracking using DVC.

Auto Label Pipeline A practical ML pipeline for data labeling with experiment tracking using DVC Goals: Demonstrate reproducible ML Use DVC to build a

Todd Cook 4 Mar 08, 2022
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022
Low-dose Digital Mammography with Deep Learning

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography ====== This repository contains

WANG-AXIS 6 Dec 13, 2022
Semantic Bottleneck Scene Generation

SB-GAN Semantic Bottleneck Scene Generation Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the f

Samaneh Azadi 41 Nov 28, 2022
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

Lbl2Vec Lbl2Vec is an algorithm for unsupervised document classification and unsupervised document retrieval. It automatically generates jointly embed

sebis - TUM - Germany 61 Dec 20, 2022
SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The SpeechBrain Toolkit SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. The goal is to create a single, flexible, and us

SpeechBrain 5.1k Jan 02, 2023
existing and custom freqtrade strategies supporting the new hyperstrategy format.

freqtrade-strategies Description Existing and self-developed strategies, rewritten to support the new HyperStrategy format from the freqtrade-develop

39 Aug 20, 2021
Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Aviv Gabbay 41 Nov 29, 2022
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 08, 2023
This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition This is the offical website for paper ''

Wanglong Lu 28 Oct 29, 2022