A POMDP formulation with spatio-temporal attention reinforcement learning improves federated client selection performance under partial visibility and data heterogeneity.
Communication-efficient learning of deep networks from decentralized data
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
FedIDM filters abnormal updates in federated learning by creating condensed data through distribution matching and rejecting updates that deviate or cause high loss on that data.
citing papers explorer
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Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
A POMDP formulation with spatio-temporal attention reinforcement learning improves federated client selection performance under partial visibility and data heterogeneity.
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Personalized Digital Health Modeling with Adaptive Support Users
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
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FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
FedIDM filters abnormal updates in federated learning by creating condensed data through distribution matching and rejecting updates that deviate or cause high loss on that data.