Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy-Reference-Cited by-同舟云学术

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Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy

Published:2023 Issue: Volume: Page:1-14
ISSN:1536-1233
Container-title:IEEE Transactions on Mobile Computing
language:
Short-container-title:IEEE Trans. on Mobile Comput.

Author:

Hu Rui¹^ORCID,Guo Yuanxiong²^ORCID,Gong Yanmin³

Affiliation:

1. Department of Computer Science & Engineering, University of Nevada, Reno, NV, USA

2. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

3. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Link

http://xplorestaging.ieee.org/ielx7/7755/4358975/10360319.pdf?arnumber=10360319

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DP-FedEwc: Differentially private federated elastic weight consolidation for model personalization;Knowledge-Based Systems;2024-11

2. Efficient Federated Learning With Enhanced Privacy via Lottery Ticket Pruning in Edge Computing;IEEE Transactions on Mobile Computing;2024-10

3. Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks;Sensors;2024-08-08

4. Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability;IEEE Internet of Things Journal;2024-08-01

5. IMFL: An Incentive Mechanism for Federated Learning With Personalized Protection;IEEE Internet of Things Journal;2024-07-01

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