Creating Privacy Preserving AI with Substra

With the recent rise of generative techniques, machine learning is at an incredibly exciting point in its history. The models powering this rise require even more data to produce impactful results, and thus it’s becoming increasingly important to explore new methods of ethically gathering data while ensuring that data privacy and security remain a top priority.

In many domains that deal with sensitive information, such as healthcare, there often isn’t enough high quality data accessible to train these data-hungry models. Datasets are siloed in different academic centers and medical institutions and are difficult to share openly due to privacy concerns about patient and proprietary information. Regulations that protect patient data such as HIPAA are essential to safeguard individuals’ private health information, but they can limit the progress of machine learning research as data scientists can’t access the volume of data required to effectively train their models. Technologies that work alongside existing regulations by proactively protecting patient data will be crucial to unlocking these silos and accelerating the pace of machine learning research and deployment in these domains.

This is where Federated Learning comes in. Check out the space we’ve created with Substra to learn more!

What is Federated Learning?

Federated learning (FL) is a decentralized machine learning technique that allows you to train models using multiple data providers. Instead of gathering data from all sources on a single server, data can remain on a local server as only the resulting model weights travel between servers.

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Conclusion

Regardless of the methods used, it's important to stay vigilant of the fact that data privacy is a right for all of us. It’s critical that we move forward in this AI boom with privacy and ethics in mind.

If you’d like to play around with Substra and implement federated learning in a project, you can check out the docs here.