To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that \emph{BGTplanner} achieves an average improvement of 6.76\% in training performance compared to state-of-the-art baselines.

翻译：为缓解日益增长的隐私泄露担忧，联邦推荐系统范式应运而生，其中分散的客户端在不暴露其原始用户-物品评分数据的情况下协同训练推荐模型。差分隐私联邦推荐系统通过向客户端注入差分隐私噪声进一步增强了联邦推荐系统的隐私保护能力。然而，当前受噪声失真影响的差分隐私联邦推荐系统难以达到令人满意的精度。已有多种研究致力于通过在学习过程中自适应分配隐私预算来改进差分隐私联邦推荐系统。但由于隐私预算分配与模型精度之间存在复杂关联，现有工作仍远未实现差分隐私联邦推荐系统精度的最大化。为解决这一挑战，我们开发了BGTplanner（预算规划器），以策略性地为每一轮差分隐私联邦推荐系统训练分配隐私预算，从而提升整体训练性能。具体而言，我们利用高斯过程回归和历史信息来预测特定分配隐私预算下推荐精度的变化。此外，通过整合当前改进与长期隐私约束，采用上下文多臂赌博机模型来制定隐私预算分配决策。我们在真实数据集上的大量实验结果表明，相较于最先进的基线方法，\emph{BGTplanner}在训练性能上平均提升了6.76\%。