CAMA combines FedZero's renewable-energy-aware client selection with HeteroFL's ordered dropout to adapt model sizes dynamically, reportedly achieving faster convergence and lower energy use in simulated federated learning.
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Energy-efficient Federated Learning with Dynamic Model Size Allocation
CAMA combines FedZero's renewable-energy-aware client selection with HeteroFL's ordered dropout to adapt model sizes dynamically, reportedly achieving faster convergence and lower energy use in simulated federated learning.