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The Importance of Hyperparameter Optimization in Federated Learning
In federated learning, doing multiple training runs is not always desirable. First of all, you have to spend more communication costs and more training costs at the edge. Every sort of new training run also spends more privacy, if you look at it from that perspective. The point is that every time you communicate something about your data, you reveal something about yourdata.