Deprecated

⚠️ **This project has been moved to: https://github.com/IKNL/vantage6-algorithms ** ⚠️

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A privacy preserving federated learning solution

# Federated Summary

⚠️ priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange (VANTAGE6)
This algorithm is part of VANTAGE6. A docker build of this algorithm can be obtained from harbor.vantage6.ai/algorithms/dsummary

Algorithm that is inspired by the Summary function in R. It report the Min, Q1, Mean, Median, Q3, Max and number of Nan values per column from each Node.

Possible Privacy Issues

🚨 Categorial column with only one category
🚨 Min an Max for each column is reported
🚨 Column names can be geussed, by trail and error

Privacy Protection

✔️ If column names do not match nothing else is reported
✔️ If dataset has less that 10 rows, no statistical analysis is performed
✔️ Only statistical results Min, Q1, Mean, Median, Q3, Max and number of Nan values per column are reported.

Usage

from vantage6.client import Client
from pathlib import Path

# Create, athenticate and setup client
client = Client("http://127.0.0.1", 5000, "")
client.authenticate("[email protected]", "password")
client.setup_encryption(None)

# Define algorithm input
# include the columns you want to summarize, 
# and specify if they are categorical ("category" or "c") or numeric ("numeric" or "n")
input_ = {
    "master": True,
    "method":"master",
    "args": [],
    "kwargs": {
        "columns": {
            "num_awards": "numeric",
            "prog": "category",
            "math": "n"
        }        
    }
}

# Send the task to the central server
task = client.task.create(name="algo_testing-summary",
                          image="harbor2.vantage6.ai/testing/summary:latest",
                          input=input_,
                          collaboration=1, 
                          organizations=[2],
                          description=""
                          )

# Retrieve the results
print("Waiting for results")
task_id = task.get("id")
task_info = client.task.get(task_id)
while not task_info.get("complete"):
    task_info = client.task.get(task_id, include_results=True)
    print("Waiting for results")
    time.sleep(3)
print("Results are ready!")

result_info = client.result.get(task_info.get("results")[0].get("id"))
result = result_info["result"]

Test / Develop

You need to have Docker installed.

To Build (assuming you are in the project-directory):

docker build -t harbor.vantage6.ai/algorithms/summary .

To test/run locally the folder local is included in the repository. The following command mounts these files and sets the docker ENVIROMENT_VARIABLE DATABASE_URI.

docker run -e DATABASE_URI=/app/database.csv -v .\local\input.txt:/app/input.txt -v .\local\output.txt:/app/output.txt -v .\local\database.csv:/app/database.csv harbor.vantage6.ai/algorithms/summary