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.
Federated active learning (f- al): an efficient annotation strategy for federated learning,
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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.