fogml 0.0.4

Tiny Federated ML for Fog Computing

FogML

Due to the development of IoT solutions, we can observe the constantly growing number of these devices in almost every aspect of our lives. The machine learning may improve increase their intelligence and smartness. Unfortunately, the highly regarded programming libraries consume to much resources to be ported to the embedded processors.

The structure of the project is as follows:

• the src folder contains the source code generators for machine learning models i.e.: naive bayes, decision trees/forrest and neural nets;
• the example folder contains the simple examples and the MNIST digit recognition for Arduino board and the simple TFT touchscreen.

Usage

pip install fogml

Example

from sklearn import datasets, tree

from fogml.generators import GeneratorFactory

iris = datasets.load_iris()
X = iris.data
y = iris.target

clf = tree.DecisionTreeClassifier(random_state=3456)
clf.fit(X, y)
print( 'accuracy: ',clf.score(X,y))

factory = GeneratorFactory()
generator = factory.get_generator(clf)
generator.generate()

FogML research

If you think that the project is interesting to you, please cite the paper: Tomasz Szydlo, Joanna Sendorek, Robert Brzoza-Woch, Enabling machine learning on resource constrained devices by source code generation of the learned models, ICCS 2018

The research was supported by the National Centre for Research and Development (NCBiR) under Grant No. LIDER/15/0144 /L-7/15/NCBR/2016.