Federated Neural Collaborative Filtering

Perifanis, Vasileios; Efraimidis, Pavlos S.

doi:10.1016/j.knosys.2022.108441

Cited by 76 publications

(20 citation statements)

References 20 publications

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“…We adopt the same hyper-parameter setting for NCF-based methods with two kinds of training methods, including the same number of latent factors, learning rate, number of MLP layers, etc. For MF methods, we refer to the results from [14,32]. It is worth mentioning that we are not attempting to tune a new state-ofthe-art recommendation performance, which is a mission better suited to much more complicated neural networks.…”

Section: Resultsmentioning

confidence: 99%

Cali3F: Calibrated Fast Fair Federated Recommendation System

Zhu¹,

Si²,

Wang³

et al. 2022

Preprint

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The increasingly stringent regulations on privacy protection have sparked interest in federated learning. As a distributed machine learning framework, it bridges isolated data islands by training a global model over devices while keeping data localized. Specific to recommendation systems, many federated recommendation algorithms have been proposed to realize the privacy-preserving collaborative recommendation. However, several constraints remain largely unexplored. One big concern is how to ensure fairness between participants of federated learning, that is, to maintain the uniformity of recommendation performance across devices. On the other hand, due to data heterogeneity and limited networks, additional challenges occur in the convergence speed. To address these problems, in this paper, we first propose a personalized federated recommendation system training algorithm to improve the recommendation performance fairness. Then we adopt a clustering-based aggregation method to accelerate the training process. Combining the two components, we proposed Cali3F, a calibrated fast and fair federated recommendation framework. Cali3F not only addresses the convergence problem by a within-cluster parameter sharing approach but also significantly boosts fairness by calibrating local models with the global model. We demonstrate the performance of Cali3F across standard benchmark datasets and explore the efficacy in comparison to traditional aggregation approaches.

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Section: Resultsmentioning

confidence: 99%

Cali3F: Calibrated Fast Fair Federated Recommendation System

Zhu¹,

Si²,

Wang³

et al. 2022

Preprint

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“…The main objective of the TR layer is to learn an unknown parameter , a worker's ability to complete any specific task. Pertaining to our model requirements, we adopt a NCF approach to learn user ability because of its ability to learn implicit preferences and also because it has been extensively studied for and applied to the reality recommender systems [25][26][27].…”

Section: Tropt-netmentioning

confidence: 99%

Applied Decision Focused Learning: An End-to-End Decision System for Task Allocation

Tungom

Ding²,

Wang³

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Continuous timely repair and replacement of infrastructures, equipment and utilities play an important role in maintaining the smooth-running of a city or local community. Thereby, to help individuals and businesses go about their daily activities with ease, it is vital to develop a proper method for automatically identifying and assigning capable workers for tasks. This paper defines the community management service task allocation problem as CMS-TAP and hence an end-to-end “recommendation + allocation” network, i.e. a task recommender and allocation optimization network (denoted as TROpt-NET), is then developed for handling such problem. TROpt-NET consists of two layers, namely one for predicting worker ability and the other for allocating tasks which are TR Layer and TA Layer, corresponding to “recommendation” and “allocation” of tasks. Different from operations research approaches where workers are assigned to jobs based on their pre-labelled skills and fixed locations, we propose a task recommender and allocation optimization network. The TR layer is a task recommender system designed to learn implicit worker abilities for different tasks using Neural Collaborative Filtering (NCF) by mining a historical dataset of worker task completion. Whereas in the TA layer a differential optimization approach for allocation is used because of its differentiable property and ability to allow for backpropagation to the prediction layer. In this study, we first formulate the CMS-TAP problem as a recommendation +optimization problem and then propose and end-to-end network architecture that tackles the problem in a real-world setting. TROpt-NET curbs uncertainty and assumptions in optimization by learning to more accurately approximate worker ability across different tasks. Additionally, the network can learn implicit worker abilities enabling optimal utilization of workers across a wide range of tasks, which is often ignored in task allocation problems. We find that normalizing worker ability across all tasks improves the implicit learning capability of the network and that good approximations don’t always lead to optimal allocation but learning allocations by backpropagating through recommendations improves the allocation objective. Offline experiments on a real-world large-scale dataset demonstrate the effectiveness of our proposed TROpt-NET.

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“…This results in users with strong privacy demands are not satisfied, whereas users with weak privacy demands pay an unnecessary recommendation performance price. Third, every user's privacy demands are not static, and traditional FedRec systems mainly focus on privacy protection in the training stage, making it difficult for users to modify privacy settings [34,36]. Consequently, the balance between utility and privacy is not flexible for users in traditional FedRec systems.…”

Section: Introductionmentioning

confidence: 99%

User Consented Federated Recommender System Against Personalized Attribute Inference Attack

Hu,

Song

2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines. Our code is available at https://github.com/HKUST-KnowComp/UC-FedRec.

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Federated Neural Collaborative Filtering

Cited by 76 publications

References 20 publications

Cali3F: Calibrated Fast Fair Federated Recommendation System

Cali3F: Calibrated Fast Fair Federated Recommendation System

Applied Decision Focused Learning: An End-to-End Decision System for Task Allocation

User Consented Federated Recommender System Against Personalized Attribute Inference Attack

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