FedQHD achieves closed-form federated Q-learning via hyperdimensional encoders with linear readouts, formalizes the federation gap under heterogeneous encoders, and reports competitive performance on continuous-state benchmarks with reduced computation.
Federated deep reinforcement learning.arXiv preprint arXiv:1901.08277
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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.
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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FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
FedQHD achieves closed-form federated Q-learning via hyperdimensional encoders with linear readouts, formalizes the federation gap under heterogeneous encoders, and reports competitive performance on continuous-state benchmarks with reduced computation.
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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.