Asynchronous probability ensembling allows heterogeneous CNNs to collaborate in federated disaster detection by exchanging class probabilities instead of weights, reducing communication and improving accuracy.
Ensemble and continual federated learning for classification tasks
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
This survey defines the Federated Continual Learning problem, proposes a taxonomy for approaches, reviews applications and metrics, and identifies open challenges in lifelong privacy-preserving learning on non-stationary distributed data.
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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Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
This survey defines the Federated Continual Learning problem, proposes a taxonomy for approaches, reviews applications and metrics, and identifies open challenges in lifelong privacy-preserving learning on non-stationary distributed data.