research-article

Fast-adapting and privacy-preserving federated recommender system

Authors:

Alexander Zhou,

Xiangliang ZhangAuthors Info & Claims

The VLDB Journal, Volume 31, Issue 5

Pages 877 - 896

https://doi.org/10.1007/s00778-021-00700-6

Published: 03 October 2021 Publication History

Abstract

In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible large-scale user data. Such data is commonly collected from users’ personal devices and then centrally stored in the cloud server to facilitate model training. However, with the rising public concerns on user privacy leakage in online platforms, online users are becoming increasingly anxious over abuses of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and strong privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user’s data is fully retained on her/his personal device while contributing to training an accurate model. On the other hand, to better embrace the data heterogeneity (e.g., users’ data vary in scale and quality significantly) in FL, we innovatively introduce a first-order meta-learning method that enables fast on-device personalization with only a few data points. Furthermore, to defend against potential malicious participants that pose serious security threat to other users, we further develop a user-level differentially private model, namely DP-PrivRec, so attackers are unable to identify any arbitrary user from the trained model. To compensate for the loss by adding noise during model updates, we introduce a two-stage training approach. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of both PrivRec and DP-PrivRec.

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Cited By

Tian CXie YChen XLi YZhao X(2024)Privacy-preserving Cross-domain Recommendation with Federated Graph LearningACM Transactions on Information Systems10.1145/365344842:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653448
Zheng RQu LChen TCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645696(3930-3939)Online publication date: 13-May-2024
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Qu LYuan WZheng RCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Towards Personalized Privacy: User-Governed Data Contribution for Federated RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645690(3910-3918)Online publication date: 13-May-2024
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cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 31, Issue 5

Sep 2022

339 pages

ISSN:1066-8888

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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 03 October 2021

Accepted: 31 August 2021

Revision received: 08 July 2021

Received: 07 January 2021

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https://dl.acm.org/doi/10.1145/3653448
Zheng RQu LChen TCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645696(3930-3939)Online publication date: 13-May-2024
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