RIFLES schedules federated learning clients by forecasting device availability with a CNN-LSTM model, claiming faster convergence and lower dropout than Random, FedCS, and REFL in simulation.
Performance analysis of smartphone-sensor behavior for human activity recognition,
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RIFLES: Resource-effIcient Federated LEarning via Scheduling
RIFLES schedules federated learning clients by forecasting device availability with a CNN-LSTM model, claiming faster convergence and lower dropout than Random, FedCS, and REFL in simulation.