QIS vs TensorFlow Federated: Why Routing Outcomes Beats Routing Gradients

You've built a federated learning pipeline with TensorFlow Federated. The architecture is sound on paper: keep raw data on-device, aggregate model updates centrally, preserve privacy by never moving the raw records. Then reality sets in. Your synchronization rounds keep failing because three of your eleven hospital sites have unpredictable network windows. Your security audit flags gradient inversion as a documented attack vector. Your bandwidth budget is being consumed by gradient tensors that grow proportionally with your model size.