Derives heterogeneity bounds separating objective-shift and feasible-set-shift effects in decision-focused federated learning and shows federation benefits when statistical gains exceed client-specific penalties.
Federated deep reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Reinforcement learning is advanced for communication-efficient federated optimization and for preference-aligned, contextually safe policies in large language models.
citing papers explorer
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Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints
Derives heterogeneity bounds separating objective-shift and feasible-set-shift effects in decision-focused federated learning and shows federation benefits when statistical gains exceed client-specific penalties.
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Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
Reinforcement learning is advanced for communication-efficient federated optimization and for preference-aligned, contextually safe policies in large language models.