Prototype system implementing the DP ADMM-sharing algorithm.

Data source can be downloaded from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html

In our submitted paper, we tested the a9a and the gisette datasets.

Before running the system, you need to go into script folder and launch split.py to split the data. Following our naming convention, the training data and testing data are located in the same folder with name "raw_train" and "raw_test" respectively. The variable "milestone" controls how many partitions to split and where to split. It indicates the starting feature index for each data partition.

The default parameter will split the a9a data into two parts of with 66 and 57 features.

Go into the folder src and launch fdml.py to run the experiment. Major parameters are set in config.py. Major run time metrics are stored in a dict and dumped to a file in the indicate output folder.

scipy 1.16.2 numpy 1.16.2 sklearn 0.20.0 pickle