<ul><li>FedRLHF is a decentralized framework for Reinforcement Learning with Human Feedback (RLHF).</li><li>It addresses privacy concerns by enabling collaborative policy learning without sharing raw data or human feedback.</li><li>The framework utilizes federated reinforcement learning, allowing each client to integrate human feedback locally into their reward functions.</li><li>Empirical evaluations demonstrate that FedRLHF preserves user privacy, achieves performance similar to centralized RLHF, and enhances personalization across different client environments.</li></ul>

FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF

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