Selecting the local examples a model is least confident about, and training only part of the model, lets federated learning use far less client data and compute while keeping or improving accuracy.
Active learning: A survey,
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Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
Selecting the local examples a model is least confident about, and training only part of the model, lets federated learning use far less client data and compute while keeping or improving accuracy.