Asynchronous probability ensembling allows heterogeneous CNNs to collaborate in federated disaster detection by exchanging class probabilities instead of weights, reducing communication and improving accuracy.
SCAFFOLD: Stochastic controlled averaging for federated learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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FedPLT assigns client-specific model layers for training and matches or beats full-model federated learning accuracy with 71-82 percent fewer trainable parameters per client.
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FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training
FedPLT assigns client-specific model layers for training and matches or beats full-model federated learning accuracy with 71-82 percent fewer trainable parameters per client.