Federated learning

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. Examples include predictive text on cell phones, a person’s engagement with their own photos, and machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and ML 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.

Ryan Micallef discusses the algorithmic and production techniques of federated learning and the privacy-preserving, fault-tolerant product opportunities they offer.