Applying federated learning to protect data on mobile devices

This post introduces a new system architecture and methodology for applying federated learning with differential privacy (FL-DP) to train ML models on mobile devices. It addresses challenges such as label balancing, feature normalization, metrics calculation due to lack of data visibility, slower mobile release cycles, slower training due to federation, and anonymized system logging. The architecture combines mobile devices, trusted execution environments, and conventional back-end servers, and has been validated with an in-house FL library compatible with Meta's apps, showing minimal performance degradation compared to conventional server-trained models.