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.
Human activity recognition via hybrid deep learning based model,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
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.