2023/02/10 by Liang Qu, Qu, Liang, Ningzhi Tang +11 · 4 citations
Business, Management and Accounting · Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Privacy, Security, and Data Protection #Recommender Systems and Techniques #Sharing Economy and Platforms
paper · pdf · doi:10.48550/arxiv.2302.10900
openalex publication_date 2023/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-order collaborative information between users and items, and these centralized CF recommendation methods inevitably lead to a high risk of user privacy leakage. Although recently proposed federated recommendation systems can mitigate the privacy problem, they either restrict the on-device local training to an isolated ego graph or rely on an additional third-party server to access other ego graphs resulting in a cumbersome pipeline, which is hard to work in practice. In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs. In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner. Furthermore, the proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques. To validate the effectiveness of the proposed SemiDFEGL, extensive experiments are conducted on three public datasets, and the results demonstrate the superiority of the proposed SemiDFEGL compared to other federated recommendation methods.