FedMed 0.3.1

A privacy-aware federated computing library to let non-trusted clients perform statistical analysis.

FedMed

A privacy-aware federated computing scheme to let non-trusted clients perform statistical analysis. Process data scattered across multiple servers, each with its own privacy policy. Merge these remote data and local (or simulated) sources.

Author: Emmanouil (Manios) Krasanakis
Contact: [e-mail]
License: Apache 2

Quickstart data server

Install FedMed with:

pip install fedmed

Data servers host your data for clients to use. Custom map operations of the map-reduce scheme are specified in the configuration file. Sometimes, you will have the same configuration for your servers and the client that uses them. Otherwise, find a first default in the example/ folder, and replace the paths to implementations with your own. Also remove any operations you do not want to support for privacy reasons.

import fedmed as fm
server = fm.Server(config="config.yaml")

:construction: Privacy policies may make fedmed.ops.private operations inexact.

Each server can contain fragments of several datasets. Load data as pandas dataframes or combinations of lists and dicts, and set them as fragments like below.

data = [1, 2, 3]  # or dict of lists, pandas dataframe, etc
server["test array part 1"] = data

Finally, run your server with a flask-supporing WSGI library, like waitress. This will let clients include it in data operations.

from waitress import serve

if __name__ == "__main__":
    serve(server.app, host="127.0.0.1", port=8000)

:globe_with_meridians: Set up a reverse proxy server to restrict who can perform operations on your system.

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Example client program

Install FedMed with:

pip install fedmed

Set up communication channels with remote data fragments (i.e., parts of the same dataset) and organize them into one dataset. Datasets are allowed to only partially match in terms of structure and operations.

import fedmed as fm
sources = [
    fm.Remote(ip="http://127.0.0.1:8000", fragment="test array part 1"),
    fm.Remote(ip="http://127.0.0.1:8000", fragment="test array part 2")
]
data = fm.FedData(sources, config="config.yaml")

Call simple operations among those described in the default configuration file config.yaml (find a first default in the example/ folder) or define new ones. The same configuration could be shared between the client and servers, but this is not mandatory; some servers may not support some capabilities, in which case some computations will fail.

Operations are performed under a map-reduce scheme. The map is performed in the servers, and the reduce on the client. Each server is left in control of both how it performs its namesake map methods, and how it distorts outcomes to comply with some privacy policy.

from fedmed.stats import sum, len
mean = sum(data) / len(data)
print('Mean', mean)

:lock: Control of map operations allows server owners to set their own privacy policies. For example, they may share new internal data compared to old ones only when enough new samples are gathered (in the interim, outcomes on older versions of the dataset will be exposed).

For the above code to run, you need to set up some devices to run at the respective ip addresses.

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Full documentation in https://fedmed.readthedocs.io .