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How Federated Learning Could Shape the Future of AI in a Privacy-Obsessed World

 

Originally published in VentureBeat, June 3, 2019

For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World, June 16-19, 2019 in Las Vegas. 

You may not have noticed, but two of the world’s most popular machine learning frameworks — TensorFlow and PyTorch — have taken steps in recent months toward privacy with solutions that incorporate federated learning.

Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model without the need for data to leave the device.

As part of the latest release of Facebook’s popular deep learning framework PyTorch last month, the company’s AI Research group rolled out Secure and Private AI, a free two-month Udacity course on the use of methods like encrypted computation, differential privacy, and federated learning. The first course began last week and is being taught by Andrew Trask, a senior research scientist at Google’s DeepMind. He’s also the leader of Openmined, a privacy-focused open source AI community that in March released PySyft to bring PyTorch and federated learning together.

“It’s not just Facebook, I think the [AI] field in general is looking at this direction pretty seriously,” PyTorch creator Soumith Chintala told VentureBeat in an interview. “Yeah, I think you will absolutely see more effort, more direction, [and] more packages, both in terms of PyTorch and others, coming in this direction for sure.”

As privacy becomes a selling point, federated learning is poised to grow in popularity among both tech giants and industries where privacy protection is required, like health care.

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About the Author:

 

Khari writes about artificial intelligence for VentureBeat and lives in San Francisco. Nachos make me happy. I adhere to VentureBeat’s ethics policy.

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