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The classical machine learning paradigm requires the aggregation of user datain a central location where machine learning practitioners can preprocess data,calculate features, tune models and evaluate performance. The advantage of thisapproach includes leveraging high performance hardware (such as GPUs) and theability of machine learning practitioners to do in depth data analysis toimprove model performance. However, these advantages may come at a cost to dataprivacy. User data is collected, aggregated, and stored on centralized serversfor model development. Centralization of data poses risks, including aheightened risk of internal and external security incidents as well asaccidental data misuse. Federated learning with differential privacy isdesigned to avoid the server-side centralization pitfall by bringing the MLlearning step to users' devices. Learning is done in a federated manner whereeach mobile device runs a training loop on a local copy of a model. Updatesfrom on-device models are sent to the server via encrypted communication andthrough differential privacy to improve the global model. In this paradigm,users' personal data remains on their devices. Surprisingly, model training inthis manner comes at a fairly minimal degradation in model performance.However, federated learning comes with many other challenges due to itsdistributed nature, heterogeneous compute environments and lack of datavisibility. This paper explores those challenges and outlines an architecturaldesign solution we are exploring and testing to productionize federatedlearning at Meta scale.

Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings