The federated model of computation has attracted much interest due to the power of federated learning. But there is much to do outside of training: federated data preparation and cleaning, post-training federated calibration and monitoring, and federated analytics to track the behaviour. In this talk, I will touch on the algorithms and systems needed by federated computation, informed by deployment experience.
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