A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems
A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems
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DOI：
10.1109/tkde.2019.2936565
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发表时间：
2021-03-01
影响因子：
8.9
通讯作者：
Zheng, Bolong
中科院分区：
文献类型：
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作者：
Zhou, Pan;Wang, Kehao;Zheng, Bolong
如今，大数据分析的蓬勃发展以及计算能力和网络带宽的限制使得独立的代理/服务提供商难以在有限的时间内从大量在线数据中为每个用户提供合适的信息。为了应对这一挑战，推荐系统（RS）可以调用一组代理来协作学习用户的偏好和品味，这被称为分布式推荐系统（DRS）。DRS可以通过请求代理彼此共享信息来提高传统RS的准确性。然而，由于大量的候选人，DRS为每个用户进行个性化推荐是具有挑战性的。此外，代理之间的信息共享引起了隐私问题。因此，我们提出了一个隐私保护的DRS在本文中，然后模型的每个服务提供商作为一个分布式的在线学习者与上下文感知。服务提供商根据用户上下文和用户的历史行为来学习用户的偏好，从而进行个性化推荐。我们采用联邦学习框架来帮助训练一个高质量的隐私保护集中式模型在大量的分布式代理，这可能是不可靠的相对较慢的网络连接。为了处理大数据场景，我们构建了一个项目聚类树来处理在线和自上而下不断增加的数据集。我们进一步考虑社会网络的结构，并提出了一个有效的算法，以避免更多的性能损失自适应。理论证明表明，该算法可以同时实现服务提供商和用户的次线性后悔和差分隐私保护。数值结果证实，我们的新框架可以处理越来越大的数据集，并在隐私保护水平和预测精度之间取得平衡。
Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.