PRIVATELY CUSTOMIZING PREFINETUNING TO BETTER MATCH USER DATA IN FEDERATED LEARNING

被引：0
|
作者：
Hou, Charlie [1 ]
Zhan, Hongyuan [2 ]
Shrivastava, Akshat [2 ]
Wang, Sid [2 ]
Livshits, Sasha [2 ]
Fanti, Giulia [1 ]
Lazar, Daniel [2 ]
机构：
[1] Department of Electrical and Computer Engineering, Carnegie Mellon University, United States
[2] Meta AI, United States
来源：
arXiv | 2023年
关键词：
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Machine learning - Privacy-preserving techniques
引用
收藏
相关论文
共 50 条
  • [1] FLaPS: Federated Learning and Privately Scaling
    Paul, Sudipta
    Sengupta, Poushali
    Mishra, Subhankar
    [J]. 2020 IEEE 17TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2020), 2020, : 13 - 19
  • [2] Data-Free Evaluation of User Contributions in Federated Learning
    Lv, Hongtao
    Zheng, Zhenzhe
    Luo, Tie
    Wu, Fan
    Tang, Shaojie
    Hua, Lifeng
    Jie, Rongfei
    Lv, Chengfei
    [J]. 2021 19TH INTERNATIONAL SYMPOSIUM ON MODELING AND OPTIMIZATION IN MOBILE, AD HOC, AND WIRELESS NETWORKS (WIOPT), 2021,
  • [3] Ensuring Security of User Data Using Federated Learning for the Sentiment Classification
    Kumar, Pravin
    Vardhan, Manu
    [J]. ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2021, 2022, 1534 : 297 - 306
  • [4] Federated Learning with Noisy User Feedback
    Sharma, Rahul
    Ramakrishna, Anil
    MacLaughlin, Ansel
    Rumshisky, Anna
    Majmudar, Jimit
    Chung, Clement
    Avestimehr, Salman
    Gupta, Rahul
    [J]. NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES, 2022, : 2726 - 2739
  • [5] Recover User's Private Training Image Data by Gradient in Federated Learning
    Gong, Haimei
    Jiang, Liangjun
    Liu, Xiaoyang
    Wang, Yuanqi
    Wang, Lei
    Zhang, Ke
    [J]. SENSORS, 2022, 22 (19)
  • [6] Mitigating data imbalance and generating better prototypes in heterogeneous Federated Graph Learning
    Kong, Xiangjie
    Yuan, Haopeng
    Shen, Guojiang
    Zhou, Hanlin
    Liu, Weiyao
    Yang, Yao
    [J]. KNOWLEDGE-BASED SYSTEMS, 2024, 296
  • [7] Learning Privately from Multiparty Data
    Hamm, Jihun
    Cao, Paul
    Belkin, Mikhail
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 48, 2016, 48
  • [8] A Federated Learning Approach for Continuous User Identification
    Veiga, R.
    Flexa, R.
    Bastos, L.
    Medeiros, I.
    Rosario, D.
    Cerqueira, E.
    Zeadally, S.
    Villas, L.
    [J]. 2023 IEEE 9TH WORLD FORUM ON INTERNET OF THINGS, WF-IOT, 2023,
  • [9] Hierarchical Personalized Federated Learning for User Modeling
    Wu, Jinze
    Liu, Qi
    Huang, Zhenya
    Ning, Yuting
    Wang, Hao
    Chen, Enhong
    Yi, Jinfeng
    Zhou, Bowen
    [J]. PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), 2021, : 957 - 968
  • [10] FedSampling: A Better Sampling Strategy for Federated Learning
    Qi, Tao
    Wu, Fangzhao
    Lyu, Lingjuan
    Huang, Yongfeng
    Xie, Xing
    [J]. PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, 2023, : 4154 - 4162
12345