Federated Multi-view Matrix Factorization for Personalized Recommendations

Flanagan, Adrian; Oyomno, Were; Grigorievskiy, Alexander; Tan, Kuan Eeik; Khan, Suleiman A.; Ammad-ud-din, Muhammad

doi:10.1007/978-3-030-67661-2_20

Cited by 48 publications

(23 citation statements)

References 17 publications

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“…They propose to use the diferential privacy [35] to limit the exposure of the data in a federated recommender system. FED-MVMF [11] extends the Multi-View Matrix Factorization (MVMF) [43] to a federated learning framework. It simultaneously factorizes both feature matrices and interaction matrices.…”

Section: Federated Learning For Recommender Systemmentioning

confidence: 99%

Federated Social Recommendation with Graph Neural Network

Liu

Yang

Fan

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel framework Fe drated So cial recommendation with G raph neural network ( FeSoG ). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

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Section: Federated Learning For Recommender Systemmentioning

confidence: 99%

Federated Social Recommendation with Graph Neural Network

Liu

Yang

Fan

et al. 2022

ACM Trans. Intell. Syst. Technol.

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“…Recently, due to users' increasing concerns on privacy leakage, some privacy-preserving recommendation methods have been proposed Flanagan et al, 2020;Lin et al, 2020;Wang et al, 2021;Yang et al, 2021;Wu et al, 2021aWu et al, , 2020a. For example, Chai et al (2019) proposed to compute gradients of user and item embeddings in user clients based on locally stored user rating data and upload gradients to the server for federated model updating.…”

Section: Privacy-preserving Recommendationmentioning

confidence: 99%

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

Qi¹,

Wu²,

Wu³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models offline and provide online recommendation services. However, user data is usually highly privacy-sensitive, and centrally storing them may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains two stages. The first one is for candidate news generation (i.e., recall) and the second one is for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.

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“…In this way, instead of transmitting (uploading/downloading) the huge payload that includes the entire global model, only part of the global model with a smaller payload is transmitted over the FL network. The users perform the standard model updates as part of the FRS Ammad-Ud-Din et al [2019], Flanagan et al [2021], thus avoiding any additional optimization steps (see Figure 1). As a case study, we have presented the payload optimization of a traditional FCF method.…”

Section: # Itemsmentioning

confidence: 99%

“…As a case study, we have presented the payload optimization of a traditional FCF method. However, the proposed method can be generalized to advanced deep learning-based FL recommendation systems and it can also be applied to a generic class of matrix factorization models Flanagan et al [2021]. We extensively compared the results from three benchmark recommendation datasets, specifically Movielens, Last-FM, and MIND.…”

Section: # Itemsmentioning

confidence: 99%

A Payload Optimization Method for Federated Recommender Systems

Khan

Flanagan²,

Tan³

et al. 2021

Fifteenth ACM Conference on Recommender Systems

Self Cite

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In this study, we introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and users depends on the number of items to recommend. The model payload grows when there is an increasing number of items. This becomes challenging for an FRS if it is running in production mode. To tackle the payload challenge, we formulated a multi-arm bandit solution that selected part of the global model and transmitted it to all users. The selection process was guided by a novel reward function suitable for FL systems. So far as we are aware, this is the first optimization method that seeks to address item dependent payloads. The method was evaluated using three benchmark recommendation datasets. The empirical validation confirmed that the proposed method outperforms the simpler methods that do not benefit from the bandits for the purpose of item selection. In addition, we have demonstrated the usefulness of our proposed method by rigorously evaluating the effects of a payload reduction on the recommendation performance degradation. Our method achieved up to a 90% reduction in model payload, yielding only a ∼4% -8% loss in the recommendation performance for highly sparse datasets 1 .

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Federated Multi-view Matrix Factorization for Personalized Recommendations

Cited by 48 publications

References 17 publications

Federated Social Recommendation with Graph Neural Network

Federated Social Recommendation with Graph Neural Network

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

A Payload Optimization Method for Federated Recommender Systems

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