Federated learning: Machine learning with privacy on the edge

Description

Federated learning is distributed machine learning across edge devices with a number of twists that make it both challenging and broadly applicable. Training happens on the same devices that generate the data. Those edge users are often concerned about privacy and are thus unwilling to share their training data. And even when they’re willing to share the data, communication is unreliable and slow, so it may not be practical.

Chris Wallace offers a demonstration of a working prototype example of federated learning applied to a predictive maintenance problem. In this demo, customers aren’t willing to share the details of how their components failed with the manufacturer but want the manufacturer to provide them with a strategy to maintain the part. Chris shows how the prototype satisfies the customer’s privacy concerns while providing them with a model that leads to fewer costly failures and less maintenance downtime.

Along the way, Chris explores other examples, such as predictive text on cell phones, a person’s engagement with their own photos, machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and machine learning on low-powered field devices in energy, agriculture, and logistics. The principles of data minimization established by the GDPR and the prevalence of smart sensors makes these use cases more common and the advantages of federated learning more compelling.

Join in to get an introduction to the algorithmic and production techniques of federated learning and the privacy-preserving, fault-tolerant product opportunities they offer.