Affiliation:
1. School of Computer and Information Technology, Beijing Jiaotong University, China
2. University of Science and Technology of China, China
Abstract
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages. However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. We argue that such a scheme in federated settings ignores the limited capacities in resource-constrained user devices (
i.e.
, storage space, computational overhead, and communication bandwidth), and makes it harder to be deployed in large-scale recommender systems. Besides, it has been shown that transmitting local gradients in real-valued form between server and clients may leak users’ private information. To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization,
LightFR
, that is able to generate high-quality binary codes by exploiting learning to hash technique under federated settings, and thus enjoys both fast online inference and economic memory consumption. Moreover, we devise an efficient federated discrete optimization algorithm to collaboratively train model parameters between the server and clients, which can effectively prevent real-valued gradient attacks from malicious parties. Through extensive experiments on four real-world datasets, we show that our LightFR model outperforms several state-of-the-art FRS methods in terms of recommendation accuracy, inference efficiency and data privacy.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference54 articles.
1. Deepak Agarwal and Bee-Chung Chen. 2010. fLDA: matrix factorization through latent dirichlet allocation. In WSDM. 91–100. Deepak Agarwal and Bee-Chung Chen. 2010. fLDA: matrix factorization through latent dirichlet allocation. In WSDM. 91–100.
2. Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman A Khan Were Oyomno Qiang Fu Kuan Eeik Tan and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888(2019). Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman A Khan Were Oyomno Qiang Fu Kuan Eeik Tan and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888(2019).
3. Rianne van den Berg Thomas N Kipf and Max Welling. 2018. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263(2018). Rianne van den Berg Thomas N Kipf and Max Welling. 2018. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263(2018).
4. Desheng Cai Shengsheng Qian Quan Fang Jun Hu and Changsheng Xu. 2022. User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network. ACM Trans. Inf. Syst.(2022). Just Accepted. Desheng Cai Shengsheng Qian Quan Fang Jun Hu and Changsheng Xu. 2022. User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network. ACM Trans. Inf. Syst.(2022). Just Accepted.
5. Sebastian Caldas Jakub Konečny H Brendan McMahan and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210(2018). Sebastian Caldas Jakub Konečny H Brendan McMahan and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210(2018).
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献