Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data-Reference-Cited by-同舟云学术

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Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data

Published:2024 Issue: Volume:19 Page:7404-7419
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Chen Zekai¹^ORCID,Yu Shengxing²,Chen Farong³,Wang Fuyi⁴^ORCID,Liu Ximeng¹^ORCID,Deng Robert H.⁵^ORCID

Affiliation:

1. College of Computer and Data Science, Fuzhou University, Fuzhou, China

2. Fujian Big Data Group Corporation, Fujian, China

3. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

4. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

5. School of Information Systems, Singapore Management University, Victoria Street, Singapore

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Fundo para o Desenvolvimento das Ciências e da Tecnologia (FDCT

General Research and Development Subsidy Program Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10614241.pdf?arnumber=10614241

Reference52 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan

2. Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

3. Toward a Personalized Clustered Federated Learning: A Speech Recognition Case Study

4. Faster Adaptive Federated Learning

5. Membership Inference Attacks Against Adversarially Robust Deep Learning Models

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