PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for Reinforcement Learning in Human-in-the-Loop Systems

arXiv:2403.05864v1 Announce Type: cross
Abstract: Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions. However, integrating RL in such settings raises significant privacy concerns, as it might inadvertently expose sensitive user information. Addressing this, our paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy through exploiting an early-exit approach designed explicitly for privacy preservation in HITL environments. This approach …

adaptability applications arxiv aware cs.cr cs.hc cs.lg dynamic exit human loop nature privacy privacy concerns settings systems