OpenAlex | Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT

Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT

Work

Year: 2021

Type: article

Abstract: Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as... more

Source: IEEE Transactions on Industrial Informatics

Authors Zuobin Xiong, Zhipeng Cai, Daniel Takabi, Wei Li

Institution Georgia State University

Cites: 33

Cited by: 122

Related to: 10

FWCI: 13.08

Citation percentile (by year/subfield): 100

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Sustainable Development Goal Partnerships for the goals

Open Access status: bronze

Funders National Science Foundation, National Science Foundation, National Science Foundation, National Science Foundation, National Science Foundation

Grant IDS 2011845, 1912753, 1829674, 1704287, 1741277