FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
This work addresses privacy and performance issues for users in federated recommendation systems, though it appears incremental as it builds on existing federated learning methods.
The paper tackled privacy and heterogeneity challenges in federated recommendation systems by proposing FedRec+, which enhances privacy through optimal subset selection for virtual ratings and addresses heterogeneity using Wasserstein distance for client weighting, achieving state-of-the-art performance on multiple datasets.
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.