federated-learning-gmm's Introduction

Federated Learning GMM

An implementation of the Gaussian Mixture Model according to federated learning paradigm

The aim of this project is demonstrating an effective implementation of the Gaussian Mixture Model (GMM) with Expectation-Maximization (EM) algorithm according to the vanilla federated learning paradigm as decribed in the paper Communication-Efficient Learning of Deep Networks from Decentralized Data.

The Gaussian Mixture Model is employed in unsupervised learning problems, especially in clustering tasks. This repository allows to execute alternatively a baseline local version of GMM and a federated distributed implementation of the same model, in order to compare their performance.

Parameters

Name	Description	Default	Baseline	Federated
`--dataset`	Name of the dataset.	blob	X	X
`--components`	Number of Gaussians to fit.	3	X	X
`--init`	Model initialization method: random or kmeans (over a 0.5% fraction of the dataset).	random	X	X
`--seed`	Number to have random consistent results across executions.	None	X	X
`--samples`	Number of samples to generate.	10000	X	X
`--features`	Number of features for each generated sample.	2	X	X
`--soft`	Specifies if cluster bounds are soft or hard.	True	X	X
`--plots_3d`	Specifies if plots are to be done in 3D or 2D.	False	X	X
`--plots_step`	Specifies the number of rounds or epochs after which saving a plot.	1	X	X
`--epochs`	Number of epochs of training.	100	X
`--rounds`	Number of rounds of training.	100		X
`--local_epochs`	Number of local epochs for each client at every round.	10		X
`--K`	Total number of clients.	100		X
`--C`	Fraction of clients to employ in each round. From 0 to 1.	0.1		X
`--S`	Number of shards for each client. If None data are assumed to be IID, otherwise are non-IID.	None		X