# Hyperparameter definitions #

Definitions of hyperparameters used in the experiment training\. One or more of these hyperparameter options might be used, depending on your framework and fusion method\.

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Hyperparameter definitions

| Hyperparameters                         | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Rounds                                  | Int value\. The number of training iterations to complete between the aggregator and the remote systems\.                                                                                                                                                                                                                                                                                                                                                               |
| Termination accuracy *(Optional)*       | Float value\. Takes `model_accuracy` and compares it to a numerical value\. If the condition is satisfied, then the experiment finishes early\.  <br>  <br>For example, `termination_predicate: accuracy >= 0.8` finishes the experiment when the mean of model accuracy for participating parties is greater than or equal to 80%\. Currently, Federated Learning accepts one type of early termination condition (model accuracy) for classification models only\. |
| Quorum *(Optional)*                     | Float value\. Proceeds with model training after the aggregator reaches a certain ratio of party responses\. Takes a decimal value between 0 \- 1\. The default is 1\. The model training starts only after party responses reach the indicated ratio value\.  <br>For example, setting this value to 0\.5 starts the training after 50% of the registered parties responded to the aggregator call\.                                                                   |
| Max Timeout *(Optional)*                | Int value\. Terminates the Federated Learning experiment if the waiting time for party responses exceeds this value in seconds\. Takes a numerical value up to 43200\. If this value in seconds passes and the `quorum` ratio is not reached, the experiment terminates\.  <br>  <br>For example, `max_timeout = 1000` terminates the experiment after 1000 seconds if the parties do not respond in that time\.                                                        |
| Sketch accuracy vs privacy *(Optional)* | Float value\. Used with XGBoost training to control the relative accuracy of sketched data sent to the aggregator\. Takes a decimal value between 0 and 1\. Higher values will result in higher quality models but with a reduction in data privacy and increase in resource consumption\.                                                                                                                                                                              |
| Number of classes                       | Int value\. Number of target classes for the classification model\. Required if "Loss" hyperparameter is:  <br>\- `auto`  <br>\- `binary_crossentropy`  <br>\- `categorical_crossentropy`  <br>                                                                                                                                                                                                                                                                         |
| Learning rate                           | Decimal value\. The learning rate, also known as *shrinkage*\. This is used as a multiplicative factor for the leaves values\.                                                                                                                                                                                                                                                                                                                                          |
| Loss                                    | String value\. The loss function to use in the boosting process\.  <br>\- `binary_crossentropy` (also known as logistic loss) is used for binary classification\.  <br>\- `categorical_crossentropy` is used for multiclass classification\.  <br>\- `auto` chooses either loss function depending on the nature of the problem\.  <br>\- `least_squares` is used for regression\.                                                                                      |
| Max Iter                                | Int value\. The total number of passes over the local training data set to train a Scikit\-learn model\.                                                                                                                                                                                                                                                                                                                                                                |
| N cluster                               | Int value\. The number of clusters to form and the number of centroids to generate\.                                                                                                                                                                                                                                                                                                                                                                                    |
| Epoch *(Optional)*                      | Int value\. The number of local training iterations to be preformed by each remote party for each round\. For example, if you set Rounds to 2 and Epochs to 5, all remote parties train locally 5 times before the model is sent to the aggregator\. In round 2, the aggregator model is trained locally again by all parties 5 times and re\-sent to the aggregator\.                                                                                                  |
| sigma                                   | Float value\. Determines how far the local model neurons are allowed from the global model\. A bigger value allows more matching and produces a smaller global model\. Default value is 1\.                                                                                                                                                                                                                                                                             |
| sigma0                                  | Float value\. Defines the permitted deviation of the global network neurons\. Default value is 1\.                                                                                                                                                                                                                                                                                                                                                                      |
| gamma                                   | Float value\. Indian Buffet Process parameter that controls the expected number of features in each observation\. Default value is 1\.                                                                                                                                                                                                                                                                                                                                  |

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**Parent topic:**[Frameworks, fusion methods, and Python versions](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fl-frames.html)

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