2min chapter

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos - #627

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

CHAPTER

How to Benchmark Performance in a Federated Setting

The motivation here is to get away from multiple training iterations to do hyperparameter optimization. We have the sub networks and then let's say you pick your sort of clients that will participate in training, then these clients are sort of allowed to optimize a specific sub network. The primary kind of performance check we had was accuracy on some kind of test set. And one of the nice things that happen in the federated setting is that despite the fact that we optimize both parameters and hyperparameters jointly, our communication costs actually go down if you didn't optimize any hyperparameters.

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