FedDPQ jointly optimizes data augmentation, pruning, quantization, and power control in federated learning to reduce edge-device energy consumption while maintaining accuracy.
Communication-efficient learning of deep networks from decentralized data,
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Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design
FedDPQ jointly optimizes data augmentation, pruning, quantization, and power control in federated learning to reduce edge-device energy consumption while maintaining accuracy.