PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning

被引:31
|
作者
Guo, Yeting [1 ]
Liu, Fang [2 ]
Cai, Zhiping [1 ]
Zeng, Hui [1 ]
Chen, Li [3 ]
Zhou, Tongqing [1 ]
Xiao, Nong [1 ]
机构
[1] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China
[2] Hunan Univ, Sch Design, Changsha, Hunan, Peoples R China
[3] Univ Louisiana Lafayette, Sch Comp & Informat, Dept Comp Sci, Lafayette, LA 70504 USA
基金
中国国家自然科学基金;
关键词
POI recommendation; federated learning; edge computing;
D O I
10.1145/3448099
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Point-of-Interest (POI) recommendation is significant in location-based social networks to help users discover new locations of interest. Previous studies on such recommendation mainly adopted a centralized learning framework where check-in data were uploaded, trained and predicted centrally in the cloud. However, such a framework suffers from privacy risks caused by check-in data exposure and fails to meet real-time recommendation needs when the data volume is huge and communication is blocked in crowded places. In this paper, we propose PREFER, an edge-accelerated federated learning framework for POI recommendation. It decouples the recommendation into two parts. Firstly, to protect privacy, users train local recommendation models and share multi-dimensional user-independent parameters instead of check-in data. Secondly, to improve recommendation efficiency, we aggregate these distributed parameters on edge servers in proximity to users (such as base stations) instead of remote cloud servers. We implement the PREFER prototype and evaluate its performance using two real-world datasets and two POI recommendation models. Extensive experiments demonstrate that PREFER strengthens privacy protection and improves efficiency with little sacrifice to recommendation quality compared to centralized learning. It achieves the best quality and efficiency and is more compatible with increasingly sophisticated POI recommendation models compared to other state-of-the-art privacy-preserving baselines.
引用
收藏
页数:25
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