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

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

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Owner
Zhenan Fan
I am a Ph.D. student in the Department of Computer Science at the University of British Columbia. You can find more about me from https://zhenanf.me.
Zhenan Fan
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