Implementation of the federated dual coordinate descent (FedDCD) method.

Last update: Sep 21, 2022

Related tags

Deep Learning FedDCD.jl

Overview

FedDCD.jl

Implementation of the federated dual coordinate descent (FedDCD) method.

Installation

To install, just call

Pkg.add("https://github.com/ZhenanFanUBC/FedDCD.jl.git")

Get data

We get data from the website of LIBSVM. To download the datasets, just call

mkdir data
cd ./data
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_test.binary.bz2
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.scale.bz2
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.scale.t.bz2
bzip2 -d rcv1_train.binary.bz2
bzip2 -d rcv1_test.binary.bz2
bzip2 -d mnist.scale.bz2
bzip2 -d mnist.scale.t.bz2

Run FedAvg for toy example.

include("experiments/PrimalMethods.jl")
RunFedAvgAndProx(
    "data/rcv1_train.binary",
    "data/rcv1_train.binary"
    1e-2,
    0.0,
    0.3,
    0.1,
    100,
    "results/toy.txt"
    )

Run FedDCD for toy example.

include("experiments/DualMethods.jl")
RunFedDCD(
    "data/rcv1_train.binary",
    "data/rcv1_train.binary"
    1e-2,
    0.3,
    0.1,
    100,
    "results/toy.txt"
    )

Citing this package

If you use FedDCD.jl for published work, we encourage you to cite the software.

Use the following BibTeX citation:

@article{fan2022dual,
      title={A dual approach for federated learning}, 
      author={Zhenan Fan and Huang Fang and Michael P. Friedlander},
      year={2022},
      eprint={2201.11183},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Credits

FedDCD.jl is developed by Zhenan Fan and Huang Fang

Implementation of the federated dual coordinate descent (FedDCD) method.

Related tags

Overview

FedDCD.jl

Installation

Get data

Run FedAvg for toy example.

Run FedDCD for toy example.

Citing this package

Credits

Owner

Zhenan Fan

Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

Videocaptioning.pytorch - A simple implementation of video captioning

Code Repository for The Kaggle Book, Published by Packt Publishing

Few-Shot Graph Learning for Molecular Property Prediction

Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentation.

One Million Scenes for Autonomous Driving

Retinal vessel segmentation based on GT-UNet

MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming

Official code for "Distributed Deep Learning in Open Collaborations" (NeurIPS 2021)

Code repository for "Stable View Synthesis".

Method for facial emotion recognition compitition of Xunfei and Datawhale .

EfficientNetv2 TensorRT int8

pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.