Applying federated learning to protect data on mobile devices

What this post added

This post details the application of federated learning with differential privacy (FL-DP) to protect user data on mobile devices. It introduces a new system architecture and methodology designed to address challenges specific to FL-DP, such as label balancing, feature normalization, and metrics calculation without data visibility, slower mobile release cycles, and slower training due to device-side federation. The architecture combines infrastructure across 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 server-trained models.