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class mindspore.train.callback.FederatedLearningManager(model, sync_frequency, sync_type='fixed', **kwargs)[source]

Manage Federated Learning during training.

Parameters

  • model (nn.Cell) – A training model.

  • sync_frequency (int) – Synchronization frequency of parameters in Federated Learning. Note that in dataset sink mode, the unit of the frequency is the number of epochs. Otherwise, the unit of the frequency is the number of steps.

  • sync_type (str) –

    Parameter synchronization type in Federated Learning. Supports [“fixed”, “adaptive”]. Default: “fixed”.

    • fixed: The frequency of parameter synchronization is fixed.

    • adaptive: The frequency of parameter synchronization changes adaptively.

Note

This is an experimental prototype that is subject to change.

step_end(run_context)[source]

Synchronization parameters at the end of step. If sync_type is “adaptive”, the synchronous frequency is adaptively adjusted here.

Parameters

run_context (RunContext) – Include some information of the model.