Federated Learning: A Distributed Machine Learning Technique Without Data Aggregation

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.