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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Communication-efficient personalized federated multi-armed bandit recommendation framework

Chen Jiasheng1,2
Qin Hang1
1. School of Computer Science, Yangtze University, Jingzhou Hubei 434023, China
2. College of Big Data & Software Engineering, Wuzhou University, Wuzhou Guangxi 543003, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0129

Abstract

The present work addresses the challenges of data heterogeneity, privacy preservation, fast communication and scalability faced in existing content-based personalized recommendation systems. To this end, this work proposes a federated learning algorithm called FTMAB (Federated Two Multi-Armed Bandit) . The algorithm employs a federated learning framework to ensure privacy preservation and manages data heterogeneity by performing global aggregation of local models through multi-armed bandit techniques. The architecture utilizes an upper confidence bound method on the server side for global arm screening recommendation, and optimizes communication through a dynamic client-side sampling strategy to aggregate user utility scores on the local client side to enhance the personalization of the recommendation. Theoretical analysis has proven that the upper bound of regret for FTMAB is O(log T) . Experiments on both synthetic and real datasets demonstrate that FTMAB consistently exhibits low regret values while concurrently achieving substantial reductions in communication cost and running time in comparison with existing methodologies. The FTMAB framework adeptly balances privacy protection, recommendation quality and communication efficiency, thereby providing an effective solution to the challenges posed by data heterogeneity and scalability in the context of personalized recommendation systems.

Foundation Support

湖北高校2020省级科研项目(2020418)
湖北省自然科学基金资助项目(2024AFB851)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0129
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-24] Accepted Paper

Cite This Article

陈家晟, 秦航. 通信高效的个性化联邦多臂赌博机推荐框架 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0129. (Chen Jiasheng, Qin Hang. Communication-efficient personalized federated multi-armed bandit recommendation framework [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0129. )

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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