FeLiX reduces wall-clock time-to-target accuracy in federated learning by up to 2.37x using lightweight availability tiers, fresh-utility client selection, and informativeness-aware aggregation without requiring oracular knowledge of client availability.
In International conference on machine learning(2019), PMLR, pp
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Robust Federated Learning Under Real-World Client Churn
FeLiX reduces wall-clock time-to-target accuracy in federated learning by up to 2.37x using lightweight availability tiers, fresh-utility client selection, and informativeness-aware aggregation without requiring oracular knowledge of client availability.