Since its introduction in 2017 [48, 42], federated learning (FL) has seen adoption by technology platforms working with private on-device data (cross-device federated learning) or proprietary server-side data (crosssilo federated learning). FL's appeal has been driven by its straightforward privacy advantages: raw data stays in the control of participating entities, with only focused updates sent for immediate aggregation, visible to the service provider. Systems that realize federated learning [18, 35, 51] run at scale today, reducing privacy risks in sensitive applications like mobile keyboards [33, 63, 21, 53] and voice assistants [12, 34]. However, basic federated learning offers an incomplete privacy story [19]: updates sent to the service provider can reveal private data unless updates are aggregated obliviously, and aggregated updates can encode individual data unless trained with a differentially private (DP) learning algorithm [30]. A dishonest service provider might log or inspect unaggregated messages, from which a great deal of information about an individual participant can be learned [15, 57]. This risk has been addressed with oblivious aggregation schemes that guarantee the service provider cannot inspect unaggregated messages, including secure multiparty computation (SMPC) from cohorts of honest devices [17], non-colluding SMPC-based secure aggregators [58], or hardware trusted execution environments (TEEs) [35].

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.