FedDSR: Daily Schedule Recommendation in a Federated Deep Reinforcement Learning Framework

Wei Huang, Jia Liu, Tianrui Li, Tianqiang Huang, Shenggong Ji, Jihong Wan

Abstract: Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasets <i>Geolife</i> and <i>Chengdu</i> . Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed <inline-formula><tex-math notation="LaTeX">$\sim\!\! 18\%$</tex-math></inline-formula> under evaluation metric <i>perimeter</i> and improve <inline-formula><tex-math notation="LaTeX">$\sim\! 0.72\%$</tex-math></inline-formula> under evaluation metric <i>ADTS</i> .