MATLAB code for paper "F3KM: Federated, Fair and fast k-means"
This repo holds the source code and scripts for reproducing the key experiments of our paper: F3KM: Federated, Fair, and Fast k-means.
Download the following datasets, and run our data_process.py, you can get the data format (DATASET_NAME.csv, DATASET_NAME_color.csv) that can be used in our Matlab codes. Then put them in DATASET_NAME folder. For instance, for Bank, one should put them in Bank/bank.csv and Bank/bank_color.csv.
Athlete https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results
Bank https://archive.ics.uci.edu/ml/datasets/bank+marketing
Census https://archive.ics.uci.edu/ml/datasets/Adult
Creditcard https://proceedings.neurips.cc/paper/2019/file/fc192b0c0d270dbf41870a63a8c76c2f-Paper.pdf
Diabetes https://archive.ics.uci.edu/ml/datasets/diabetes
Recruitment https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring/
Spanish https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring/
Student https://analyse.kmi.open.ac.uk/open_dataset
Census1990 https://proceedings.neurips.cc/paper/2019/file/fc192b0c0d270dbf41870a63a8c76c2f-Paper
HMDA https://ffiec.cfpb.gov/data-browser/
There are 2 functions/APIs to be used from the files here (F3KM and CDKM). Try running main.m. See main.m for an example on how to use all 2 function.
Inputs: X, label,c, color, delta, block_size ,rho_0, u_0, violation, max_iters Description of Inputs: X: dataset.
label: initial label of each point, we use kmeans++ for initialization.
c: number of clusters.
color: the color matrix P in our paper.
delta: a hyperparameter for tuning alpha and delta.
block_size: n_b in our paper, the number of points in each block.
rho_0: the step size.
u_0: the initial value of dual variable.
violation: the additive violation in our paper.
max_iters: the maximun iterations.
Return: The function returns five numeric values. The first is the final label vector, the second is the objective function value, the third is the number of iterations, the fourth is the objective function values in each iteration, the fifth is the balance values in each cluster.
Inputs: X, label,c, color
For description of inputs, view above. See sample usage in main.m
Return: Y, minO, iter_num, obj, balance_value