Trends Report

Federated Learning: A Distributed Machine Learning Technique Without Data Aggregation

Build Powerful AI Models Leveraging Multiparty Data While Preserving Privacy

March 24th, 2022
Danny Mu, null
Danny Mu
With contributors:
Frederic Giron , Brandon Purcell , Mike Gualtieri , Srividya Sridharan , Tianchen Feng , Bill Nagel

Summary

A lack of sufficient high-quality data is a key barrier to training high-performance AI models. Data privacy concerns and regulations also make it difficult or even illegal to share data among parties. Federated learning (FL) is an effective way to train AI models without directly exchanging raw data while delivering model performance that is close to or on par with a model trained with aggregated data. This report shows firms lacking high-quality training data how federated learning can overcome some of their data challenges to help them unleash the power of AI.

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