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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