具有隐私意识的结构化客户层图的分散式联合推荐

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhitao Li, Zhaohao Lin, Feng Liang, Weike Pan, Qiang Yang, Zhong Ming
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引用次数: 0

摘要

推荐模型被广泛应用于各种商业应用中,为用户提供个性化服务。然而,大多数推荐模型都依赖于用户的原始评分记录,而这些记录通常是由一个集中式服务器收集的,用于模型训练,这可能会引起隐私问题。最近,为了保护用户隐私,人们提出了一些集中式联合推荐模型,但这些模型在整个模型训练过程中需要服务器来协调。作为回应,我们提出了一种新颖的隐私感知分散式联合推荐(DFedRec)模型,与传统模型相比,该模型在推荐性能上是无损的,因此比其他同类模型更准确。具体来说,在模型训练过程中,我们设计了一个隐私感知的结构化客户端级图来共享模型参数,这是一种一石二鸟的策略,即通过一些随机抽样的假条目来保护用户隐私,并通过只与相关的相邻用户共享模型参数来降低通信成本。借助隐私感知结构化客户级图,我们提出了两种新颖的无服务器协作训练机制,包括批处理算法 DFedRec(b) 和随机算法 DFedRec(s),前者需要匿名机制,后者则不需要。它们都等同于在集中服务器中训练的 PMF,因此是无损的。然后,我们对我们方法的隐私保证进行了形式分析,并在三个有明确反馈的公共数据集上进行了广泛的实证研究,结果表明了我们的 DFedRec 的有效性,即它具有隐私意识、通信效率和无损性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decentralized Federated Recommendation with Privacy-Aware Structured Client-Level Graph

Recommendation models are deployed in a variety of commercial applications in order to provide personalized services for users.

However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, which may cause privacy issues.

Recently, some centralized federated recommendation models are proposed for the protection of users’ privacy, which however requires a server for coordination in the whole process of model training.

As a response, we propose a novel privacy-aware decentralized federated recommendation (DFedRec) model, which is lossless compared with the traditional model in recommendation performance and is thus more accurate than other models in this line.

Specifically, we design a privacy-aware structured client-level graph for the sharing of the model parameters in the process of model training, which is a one-stone-two-bird strategy, i.e., it protects users’ privacy via some randomly sampled fake entries and reduces the communication cost by sharing the model parameters only with the related neighboring users.

With the help of the privacy-aware structured client-level graph, we propose two novel collaborative training mechanisms in the setting without a server, including a batch algorithm DFedRec(b) and a stochastic one DFedRec(s), where the former requires the anonymity mechanism while the latter does not. They are both equivalent to PMF trained in a centralized server and are thus lossless.

We then provide formal analysis of privacy guarantee of our methods and conduct extensive empirical studies on three public datasets with explicit feedback, which show the effectiveness of our DFedRec, i.e., it is privacy aware, communication efficient, and lossless.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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