FedDSL: A Novel Client Selection Method to Handle Statistical Heterogeneity in Cross-Silo Federated Learning Using Flower Framework

被引：0
|
作者：
Pais, Vineetha [1 ]
Rao, Santhosha [1 ]
Muniyal, Balachandra [1 ]
机构：
[1] Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Information and Communication Technology, Karnataka, Manipal,576104, India
来源：
IEEE Access | 2024年 / 12卷
关键词：
D O I：
10.1109/ACCESS.2024.3482388
中图分类号：
学科分类号：
摘要：
45
引用
收藏
页码：159648 / 159659
相关论文
共 10 条
  • [1] FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity
    Qin, Zhen
    Deng, Shuiguang
    Zhao, Mingyu
    Yan, Xueqiang
    [J]. PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 1954 - 1964
  • [2] FLZip: An Efficient and Privacy-Preserving Framework for Cross-Silo Federated Learning
    Feng, Xiaojie
    Du, Haizhou
    [J]. IEEE CONGRESS ON CYBERMATICS / 2021 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS (ITHINGS) / IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) / IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) / IEEE SMART DATA (SMARTDATA), 2021, : 209 - 216
  • [3] Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
    Tuong Do
    Nguyen, Binh X.
    Vuong Pham
    Toan Tran
    Tjiputra, Erman
    Tran, Quang D.
    Anh Nguyen
    [J]. 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 19352 - 19362
  • [4] ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
    Majeed, Umer
    Khan, Latif U.
    Yousafzai, Abdullah
    Han, Zhu
    Park, Bang Ju
    Hong, Choong Seon
    [J]. IEEE ACCESS, 2021, 9 : 155634 - 155650
  • [5] VCFL: A verifiable and collusion attack resistant privacy preserving framework for cross-silo federated learning
    Du, Weidong
    Li, Min
    Yang, Xiaoyuan
    Wu, Liqiang
    Zhou, Tanping
    [J]. PERVASIVE AND MOBILE COMPUTING, 2022, 86
  • [6] Robust Cross-Silo Federated Fraudulent Transaction Detection in Banks Using Epsilon Cluster Selection
    Myalil D.
    Rajan M.A.
    Apte M.
    Lodha S.
    [J]. SN Computer Science, 4 (4)
  • [7] A Cross-Client Coordinator in Federated Learning Framework for Conquering Heterogeneity
    Huang, Sheng
    Fu, Lele
    Li, Yuecheng
    Chen, Chuan
    Zheng, Zibin
    Dai, Hong-Ning
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024,
  • [8] FedDCS: A distributed client selection framework for cross device federated learning
    Panigrahi, Monalisa
    Bharti, Sourabh
    Sharma, Arun
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2023, 144 : 24 - 36
  • [9] NIFL: A Statistical Measures-Based Method for Client Selection in Federated Learning
    Mohamed, M'haouach
    Houdou, Anass
    Alami, Hamza
    Fardousse, Khalid
    Berrada, Ismail
    [J]. IEEE ACCESS, 2022, 10 : 124766 - 124776
  • [10] A novel hybrid strategy based on Swarm and Heterogeneous Federated Learning using model credibility awareness for activity recognition cross-silo multistorey building
    Jamil, Harun
    Khan, Murad Ali
    Jamil, Faisal
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 138
1