fl4health.clients.fedpm_client module¶
- class FedPmClient(data_path, metrics, device, loss_meter_type=LossMeterType.AVERAGE, checkpoint_and_state_module=None, reporters=None, progress_bar=False, client_name=None)[source]¶
Bases:
BasicClient- __init__(data_path, metrics, device, loss_meter_type=LossMeterType.AVERAGE, checkpoint_and_state_module=None, reporters=None, progress_bar=False, client_name=None)[source]¶
Client implementing the FedPM algorithm (https://arxiv.org/pdf/2209.15328). FedPM is a recent sparse, communication efficient approach to federated learning. The method has been shown to have exceptional information compression while maintaining good performance. Interestingly, it is also connected to the Lottery Ticket Hypothesis. Training on the client-side is effectively the same as BasicClient. The two components that change are ensuring that the model to be training is a Masked Model compatible with FedPM (or to convert it to one). Second, we use the FedPM exchanger to facilitate exchange with the server.
- Parameters:
data_path (Path) – path to the data to be used to load the data for client-side training
metrics (Sequence[Metric]) – Metrics to be computed based on the labels and predictions of the client model
device (torch.device) – Device indicator for where to send the model, batches, labels etc. Often ‘cpu’ or ‘cuda’
loss_meter_type (LossMeterType, optional) – Type of meter used to track and compute the losses over each batch. Defaults to LossMeterType.AVERAGE.
checkpoint_and_state_module (ClientCheckpointAndStateModule | None, optional) – A module meant to handle both checkpointing and state saving. The module, and its underlying model and state checkpointing components will determine when and how to do checkpointing during client-side training. No checkpointing (state or model) is done if not provided. Defaults to None.
reporters (Sequence[BaseReporter] | None, optional) – A sequence of FL4Health reporters which the client should send data to. Defaults to None.
progress_bar (bool, optional) – Whether or not to display a progress bar during client training and validation. Uses tqdm. Defaults to False
client_name (str | None, optional) – An optional client name that uniquely identifies a client. If not passed, a hash is randomly generated. Client state will use this as part of its state file name. Defaults to None.