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vantage6

A privacy preserving federated learning solution


Age Standerdized Incidence Rate

⚠️ 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/asr

It reports the local_crude_rate, combined_crude_rate, local_adjusted_rate and combined_adjusted_rate from each Node.

Possible Privacy Issues

🚨 Column names can be geussed, by trail and error

Privacy Protection

✔️ If column names do not match nothing is reported
✔️ If dataset has less that 10 columns, no statistical analysis is performed
✔️ Only statistical results in the form of aggregated data local_crude_rate, global_crude_rate, local_adjusted_rate and global_adjusted_rate are reported

Usage

from vantage6.client import Client
from pathlib import Path
import pandas as pd

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

# Define algorithm input
input_ = {
        "method": "master",
        "master": True,
        "kwargs": {
            "incidence": "incidence",
            "population": "pop",
            "gender": "sex",
            "ageclass": "agec",
            "prefacture": "pref",
            "standard_popultation": pd.read_excel('/path/to/file')
        }
    }

# Send the task to the central server
task = client.post_task(
    name="testing",
    image="harbor.vantage6.ai/algorithms/asr",
    collaboration_id=1,
    input_= input_,
    organization_ids=[1]
)

# Retrieve the results
res = client.get_results(task_id=task.get("id"))

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