Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two consecutive rounds of user-uploaded gradients. Moreover, federated recommendation systems (FRS) face the challenge of heterogeneity, leading to decreased recommendation performance. In this paper, we propose FedRec+, an ensemble framework for FRS that enhances privacy while addressing the heterogeneity challenge. FedRec+ employs optimal subset selection based on feature similarity to generate near-optimal virtual ratings for pseudo items, utilizing only the user's local information. This approach reduces noise without incurring additional communication costs. Furthermore, we utilize the Wasserstein distance to estimate the heterogeneity and contribution of each client, and derive optimal aggregation weights by solving a defined optimization problem. Experimental results demonstrate the state-of-the-art performance of FedRec+ across various reference datasets.

翻译：在推荐系统中，保护边缘用户隐私和降低通信成本是重大挑战。尽管联邦学习通过避免客户端与服务器之间的数据交换已被证明能有效保护隐私，但研究表明服务器仍可基于用户两轮连续上传梯度中的更新非零梯度推断用户评分。此外，联邦推荐系统面临异质性挑战，导致推荐性能下降。本文提出FedRec+，一种面向联邦推荐系统的集成框架，在解决异质性问题的同时增强隐私保护。FedRec+基于特征相似性采用最优子集选择，仅利用用户本地信息为伪项目生成近乎最优的虚拟评分。该方法在不增加通信成本的前提下降低了噪声干扰。进一步，我们利用Wasserstein距离估算各客户端的异质性与贡献度，并通过求解定义的优化问题推导最优聚合权重。实验结果表明，FedRec+在多个基准数据集上均取得了最先进的性能。