EH-FedSAG achieves higher test accuracy and lower training variance than EH-FedAvg in simulations of energy-harvesting federated learning for both homogeneous and heterogeneous data, with larger gains under scarce energy.
Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint,
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EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT
EH-FedSAG achieves higher test accuracy and lower training variance than EH-FedAvg in simulations of energy-harvesting federated learning for both homogeneous and heterogeneous data, with larger gains under scarce energy.