FedPCL-CDR：面向隐私保护跨域推荐的联邦原型对比学习框架
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation
AI总结：
针对跨域推荐中的隐私泄露与重叠用户稀疏问题，提出FedPCL-CDR联邦原型对比学习框架，利用LDP差分原型和非重叠用户信息实现隐私保护的知识迁移，并在Amazon、Douban上超越SOTA基线。
摘要：
跨域推荐（CDR）旨在通过从数据丰富的域迁移知识来提升稀疏域中的推荐准确性。然而，现有CDR方法通常假设跨域的用户-物品交互数据可公开获取，忽视了用户隐私问题。此外，由于依赖大量完全共享用户进行知识迁移，当重叠用户稀疏时，它们会出现性能下降。为应对这些挑战，我们提出了一种用于隐私保护CDR的联邦原型对比学习（CL）框架，称为FedPCL-CDR。该方法在联邦学习框架内利用非重叠用户信息和差分原型来提升模型性能。FedPCL-CDR包含两个关键模块：本地域（客户端）学习和全局服务器聚合。在本地域中，FedPCL-CDR首先对所有用户数据进行聚类，并利用本地差分隐私（LDP）学习差分原型，从而有效利用非重叠用户信息并保护用户隐私。随后，它以CL方式使用本地原型和服务器返回的全局原型进行知识迁移。同时，全局服务器聚合从本地域发送的差分原型，以学习本地和全局原型。在Amazon和Douban数据集上的四个CDR任务进行的大量实验表明，FedPCL-CDR优于SOTA基线。我们在https://github.com/Lili1013/FedPCL CDR发布代码。
英文摘要：
Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR approaches often assume that user-item interaction data across domains is publicly available, neglecting user privacy concerns. Additionally, they experience performance degradation with sparse overlapping users due to their reliance on a large number of fully shared users for knowledge transfer. To address these challenges, we propose a Federated Prototype-based Contrastive Learning (CL) framework for Privacy Preserving CDR, called FedPCL-CDR. This approach utilizes non-overlapping user information and differential prototypes to improve model performance within a federated learning framework. FedPCL-CDR comprises two key modules: local domain (client) learning and global server aggregation. In the local domain, FedPCL-CDR first clusters all user data and utilizes local differential privacy (LDP) to learn differential prototypes, effectively utilizing non-overlapping user information and protecting user privacy. It then conducts knowledge transfer by employing both local and global prototypes returned from the server in a CL manner. Meanwhile, the global server aggregates differential prototypes sent from local domains to learn both local and global prototypes. Extensive experiments on four CDR tasks across Amazon and Douban datasets demonstrate that FedPCL-CDR surpasses SOTA baselines. We release our code at https://github.com/Lili1013/FedPCL CDR
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
