Methods and Prospects of Personalized Federated Learning

被引：0
|
作者：
Sun, Yanhua [1 ,2 ]
Wang, Zihang [1 ,2 ]
Liu, Chang [1 ,2 ]
Yang, Ruizhe [1 ,2 ]
Li, Meng [1 ,2 ]
Wang, Zhuwei [1 ,2 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing,100124, China
[2] Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing,100124, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 20期
关键词：
Data privacy - Differential privacy;
D O I：
10.3778/j.issn.1002-8331.2403-0207
中图分类号：
学科分类号：
摘要：
Currently, with the advancement of artificial intelligence research, artificial intelligence is being widely adopted, and the increasing demand in areas such as data governance has led to growing awareness and concern for privacy protection, this has promoted the popularity of the federated learning (FL) framework. However, existing FL frameworks struggle to address heterogeneous issues and personalized user needs. In response to these challenges, methods of personalized federated learning (PFL) are studied and prospects are proposed. Firstly, the FL framework is outlined and its limitations are identified, leading to the research motivation for PFL based on FL scenarios. Subsequently, the analysis of statistical heterogeneity, model heterogeneity, communication heterogeneity, and device heterogeneity in PFL is conducted, and feasible solutions are proposed. Then, personalized algorithms in PFL such as client selection and knowledge distillation are categorized, and their innovations and shortcomings are analyzed. Finally, future research directions for PFL are discussed. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
引用
收藏
页码：68 / 83
相关论文
共 50 条
  • [1] Sparse Personalized Federated Learning
    Liu, Xiaofeng
    Li, Yinchuan
    Wang, Qing
    Zhang, Xu
    Shao, Yunfeng
    Geng, Yanhui
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (09) : 12027 - 12041
  • [2] Benchmark for Personalized Federated Learning
    Matsuda, Koji
    Sasaki, Yuya
    Xiao, Chuan
    Onizuka, Makoto
    IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY, 2024, 5 : 2 - 13
  • [3] Personalized Subgraph Federated Learning
    Baek, Jinheon
    Jeong, Wonyong
    Jin, Jiongdao
    Yoon, Jaehong
    Hwang, Sung Ju
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202, 2023, 202
  • [4] Toward Personalized Federated Learning
    Tan, Alysa Ziying
    Yu, Han
    Cui, Lizhen
    Yang, Qiang
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (12) : 9587 - 9603
  • [5] A Personalized Quantum Federated Learning
    Gurung, Dev
    Pokhrel, Shiva Raj
    PROCEEDINGS OF THE 8TH ASIA-PACIFIC WORKSHOP ON NETWORKING, APNET 2024, 2024, : 175 - 176
  • [6] Prototype Contrastive Learning for Personalized Federated Learning
    Deng, Siqi
    Yang, Liu
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT III, 2023, 14256 : 529 - 540
  • [7] Application of personalized federated learning methods to environmental sound classification: A comparative study
    Xu, Huaxing
    Fan, Zeng
    Liu, Xudong
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 135
  • [8] Personalized Federated Learning with Parameter Propagation
    Wu, Jun
    Bao, Wenxuan
    Ainsworth, Elizabeth
    He, Jingrui
    PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 2594 - 2605
  • [9] Personalized Federated Contrastive Learning for Recommendation
    Wang, Shanfeng
    Zhou, Yuxi
    Fan, Xiaolong
    Li, Jianzhao
    Lei, Zexuan
    Gong, Maoguo
    IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2025,
  • [10] Personalized Federated Learning with Semisupervised Distillation
    Li, Xianxian
    Gong, Yanxia
    Liang, Yuan
    Wang, Li-e
    SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
12345