Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method.

翻译：联邦推荐系统（FedRecs）因能将用户隐私数据保留在本地，仅向服务器传递模型参数/梯度以保护用户隐私，而备受关注。然而，现有FedRecs架构假设所有用户均具有相同的零隐私预算，即不向服务器上传任何数据，从而忽视了那些对隐私关注度较低、愿意上传数据以获取更优推荐服务的用户。为弥合这一差距，本文探索了一种用户主导的数据贡献联邦推荐架构，其中用户可自由控制是否共享数据及共享数据比例。为此，本文提出了一种名为CDCGNNFed的云-设备协作图神经网络联邦推荐模型。该模型在本地训练以用户为中心的自我图，并通过对比学习方式，在服务器上基于用户共享数据协同训练高阶图。此外，采用图修补策略预测服务器上图中的缺失连接，从而充分利用图神经网络在高阶图上的能力。在两个公开数据集上进行了大量实验，结果证明了所提方法的有效性。