Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through a central server. However, most existing federated recommender systems adopt a one-size-fits-all assumption on user privacy, where all users are required to keep their data strictly local. This setting overlooks users who are willing to share their data with the server in exchange for better recommendation performance. Although several recent studies have explored personalized user data sharing in FedRS, they assume static user privacy preferences and cannot handle user requests to remove previously shared data and its corresponding influence on the trained model. To address this limitation, we propose FedShare, a federated learn-unlearn framework for recommender systems with personalized user data sharing. FedShare not only allows users to control how much interaction data is shared with the server, but also supports data unsharing requests by removing the influence of the unshared data from the trained model. Specifically, FedShare leverages shared data to construct a server-side high-order user-item graph and uses contrastive learning to jointly align local and global representations. In the unlearning phase, we design a contrastive unlearning mechanism that selectively removes representations induced by the unshared data using a small number of historical embedding snapshots, avoiding the need to store large amounts of historical gradient information as required by existing federated recommendation unlearning methods. Extensive experiments on three public datasets demonstrate that FedShare achieves strong recommendation performance in both the learning and unlearning phases, while significantly reducing storage overhead in the unlearning phase compared with state-of-the-art baselines.

翻译：联邦推荐系统(FedRS)作为一种保护用户隐私的范式应运而生，其将交互数据保留在本地设备，同时通过中央服务器协调模型训练。然而，现有大多数联邦推荐系统对用户隐私采用"一刀切"假设，要求所有用户必须严格保持数据本地化。这种设置忽略了那些愿意为获得更优推荐性能而向服务器共享数据的用户。尽管近期若干研究探索了FedRS中的个性化用户数据共享，但它们假设用户隐私偏好是静态的，且无法处理用户撤回已共享数据及其对已训练模型影响的请求。为突破此限制，我们提出FedShare——一个支持个性化用户数据共享的联邦学习-遗忘推荐框架。FedShare不仅允许用户控制向服务器共享的交互数据量，还支持通过从已训练模型中消除未共享数据的影响来实现数据撤销请求。具体而言，FedShare利用共享数据构建服务器端高阶用户-物品图，并采用对比学习实现本地表征与全局表征的联合对齐。在遗忘阶段，我们设计了对比遗忘机制，通过少量历史嵌入快照选择性移除未共享数据诱导的表征，避免了现有联邦推荐遗忘方法需要存储大量历史梯度信息的问题。在三个公开数据集上的大量实验表明，FedShare在学习和遗忘阶段均实现了优异的推荐性能，同时在遗忘阶段相比最先进的基线方法显著降低了存储开销。