Federated Constrastive Learning and Visual Transformers for Personal Recommendation

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Asma Belhadi, Youcef Djenouri, Fabio Augusto de Alcantara Andrade, Gautam Srivastava
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引用次数: 0

Abstract

This paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learning and trusted authority mechanisms. On the other hand, it deals with data quantity, and quality by exploring both transformers and consumer clustering. The process starts by clustering the consumers into similar clusters using contrastive learning and k-means algorithm. The local model of each consumer is trained on the local data. The local models of the consumers with the clustering information are then sent to the server, where integrity verification is performed by a trusted authority. Instead of traditional federated learning solutions, two kinds of aggregation are performed. The first one is the aggregation of all models of the consumers to derive the global model. The second one is the aggregation of the models of each cluster to derive a local model of similar consumers. Both models are sent to the consumers, where each consumer decides which appropriate model might be used for personal recommendation. Robust experiments have been carried out to demonstrate the applicability of the method using MovieLens-1M, and Amazon-book. The results reveal the superiority of the proposed method compared to the baseline methods, where it reaches an average accuracy of 0.27, against the other methods that do not exceed 0.25.

Abstract Image

用于个人推荐的联合构造学习和视觉转换器
本文为消费电子应用中的个人推荐介绍了一种新颖的解决方案。一方面,它通过探索联合学习和可信权威机制,解决了训练过程中的数据保密问题。另一方面,它通过探索转换器和消费者聚类来解决数据数量和质量问题。在这一过程中,首先使用对比学习和 k-means 算法将消费者聚类为相似的群组。每个消费者的本地模型都是在本地数据上训练出来的。然后,消费者的本地模型和聚类信息被发送到服务器,由受信任的机构进行完整性验证。与传统的联合学习解决方案不同,有两种聚合方式。第一种是聚合消费者的所有模型,得出全局模型。第二种是聚合每个集群的模型,得出类似消费者的本地模型。这两个模型都会发送给消费者,由每个消费者决定哪一个合适的模型可用于个人推荐。为了证明该方法的适用性,我们使用 MovieLens-1M 和 Amazon-book 进行了大量实验。实验结果表明,与基线方法相比,所提出的方法更胜一筹,其平均准确率达到 0.27,而其他方法的平均准确率不超过 0.25。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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