App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

Privacy-preserving decentralized recommendation system based on blockchain and federated learning

Guo Jianlana
Chen Yuqiangb
Lu Rongb
Li Guangchengb
a. College of Electronic and Information Engineering, b. College of Artificial Intelligence, Dongguan Polytechnic, Dongguan Guangdong 523808, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0301

Abstract

With the overload of internet information, intelligent recommendation systems have emerged and are widely used to recommend products, content, and services to specific users. Yet, these systems require large amounts of user data to train their models. How to ensure the privacy and security of user data while maintaining model performance has become a pressing issue. Traditional recommendation systems suffer from problems such as data sparsity, and sharing raw data directly can also violate user privacy. This paper proposes a privacy-preserving decentralized federated learning recommendation system. It utilizes the peer-to-peer network and immutable data storage features of blockchain to ensure data security and system decentralization. In this system, user data is decomposed into private parameters (containing privacy information) and public parameters (containing item feature information) through matrix factorization. Users train locally, keep their private parameters, and share only the public parameters, which protects user privacy. Blockchain is introduced to coordinate the training process, where its leader aggregates local public parameters into a global public parameter. Users then download and synchronize this parameter to conduct the next training round. Furthermore, a high-performance, low-consumption consensus mechanism based on a dynamic random seed algorithm is proposed, and the model's privacy-preserving performance is analyzed. Experiments show that this system is superior to traditional centralized learning frameworks in terms of both privacy protection and recommendation accuracy. while also offering strong scalability and practical usability.

Foundation Support

广东省自然科学基金资助项目(2020A1511110162)
广东省哲学社会科学规划项目(GD25CSH06)
广东省普通高校创新团队项目(2025KCXTD094)
广东省普通高校特色创新项目(2025KTSCX373)
2021年东莞市农村振兴战略专项基金资助项目(20211800400102)
东莞市松山湖企业特派员项目(20234384-01KCJ-G,20234369-01KCJ-G,20234400-01KCJ-G)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0301
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-18] Accepted Paper

Cite This Article

郭剑岚, 陈俞强, 卢荣, 等. 基于区块链和联邦学习的隐私保护去中心化推荐系统 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0301. (Guo Jianlan, Chen Yuqiang, Lu Rong, et al. Privacy-preserving decentralized recommendation system based on blockchain and federated learning [J]. Application Research of Computers, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0301. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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