The Sherpa.ai Federated Learning and Differential Privacy Framework has been developed to facilitate open research in the field, with the objective of building models that learn from decentralized data, preserving data privacy. It is an open-source platform and aims to support 100 percent of the AI algorithms used in industry.
The Sherpa.ai Federated Learning and Differential Privacy Framework is an open-source framework for Machine Learning that allows collaborative learning to take place, without sharing private data. It has been developed to facilitate open research and experimentation in Federated Learning and Differential Privacy. Federated learning is a machine learning paradigm aimed at learning models from decentralized data, such as data located on users’ smartphones, in hospitals, or banks, and ensuring data privacy. This is achieved by training the model locally in each node (e.g., on each smartphone, at each hospital, or at each bank), sharing the model-updated parameters (not the data) and securely aggregating them to build a better global model. Federated Learning can be combined with Differential Privacy to ensure a higher degree of privacy. Differential Privacy is a statistical technique to provide data aggregations, while avoiding the leakage of individual data records. This technique ensures that malicious agents intervening in the communication of local parameters can not trace this information back to the data sources, adding an additional layer of data privacy.
This technology could be disruptive in cases where it is compulsory to ensure data privacy, as in the following examples:
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When data contains sensitive information, such as email accounts, personalized recommendations, and health information, applications should employ data privacy mechanisms to learn from a population of users whilst the sensitive data remains on each user’s device.
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When data is located in data silos, an automotive parts manufacturer, for example, may be reluctant to disclose their data, but would benefit from models that learn from other manufacturers' data, in order to improve production and supply chain management.
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Due to data-privacy legislation, banks and telecom companies, for example, cannot share individual records, but would benefit from models that learn from data across several entities.
Sherpa.ai is focused on democratizing Federated Learning by providing methodologies, pipelines, and evaluation techniques specifically designed for Federated Learning. The Sherpa.ai Federated Learning Platform enables developers to simulate Federated Learning scenarios with models, algorithms, and data provided by the framework, as well as their own data.
The Sherpa.ai Federated Learning and Differential Privacy Framework is a project by Sherpa.ai, in collaboration with the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) research group from the University of Granada.
See the Installation documentation for instructions on how to install the Sherpa.ai Federated Learning and Differential Privacy Framework.
See the Getting Started documentation for a brief introduction to using the Sherpa.ai Federated Learning and Differential Privacy Framework.
If you are interested in contributing to the Sherpa.ai Federated Learning and Differential Privacy Framework with tutorials, datasets, models, aggregation mechanisms, or any other code that others could benefit from, please be sure to review the contributing guidelines.
Use GitHub issues for tracking requests and bugs.
Please direct questions to Sherpa Developers Slack.