Asynchronous probability ensembling allows heterogeneous CNNs to collaborate in federated disaster detection by exchanging class probabilities instead of weights, reducing communication and improving accuracy.
Federated optimization in heterogeneous networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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UNVERDICTED 2representative citing papers
DeepFedNAS delivers up to 1.21% higher accuracy and 61x faster architecture search for federated learning on heterogeneous IoT by replacing random supernet sampling with Pareto-optimal elite architectures and using a multi-objective fitness function as a zero-cost proxy.
citing papers explorer
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Asynchronous Probability Ensembling for Federated Disaster Detection
Asynchronous probability ensembling allows heterogeneous CNNs to collaborate in federated disaster detection by exchanging class probabilities instead of weights, reducing communication and improving accuracy.
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DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training
DeepFedNAS delivers up to 1.21% higher accuracy and 61x faster architecture search for federated learning on heterogeneous IoT by replacing random supernet sampling with Pareto-optimal elite architectures and using a multi-objective fitness function as a zero-cost proxy.