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.

FedRec + 是一个用于增强隐私保护和解决异构性挑战的 Federated Recommendation Systems（FRS）集成框架，通过基于特征相似性的最优子集选择生成近似最优的伪评分，利用用户本地信息，减少噪声，有效降低沟通成本，并利用 Wasserstein 距离估计每个客户端的异构性和贡献，通过解决优化问题而获得最佳聚合权重，实验证明了 FedRec + 在各种参考数据集上的卓越性能。