Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses-Reference-Cited by-同舟云学术

Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses

Author:

Cui Laizhong¹^ORCID,Ma Jiating¹^ORCID,Zhou Yipeng²^ORCID,Yu Shui³^ORCID

Affiliation:

1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

2. School of Computing, FSE, Macquarie University, Sydney, NSW, Australia

3. School of Computer Science, University of Technology Sydney, Ultimo, NSW, Australia

Funder

National Key Research and Development Plan of China

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

Reference35 articles.

2. On the practicality of differential privacy in federated learning by tuning iteration times;fu,2021

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2. NeuronDP: Neuron-grained Differential Privacy of Deep Neural Networks in Edge-based Federated Learning;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24