Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability-Reference-Cited by-同舟云学术

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Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability

Published:2024-08-01 Issue:15 Volume:11 Page:26430-26443
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Sun Xuehua¹,Yuan Zengsen²^ORCID,Kong Xianguang¹,Xue Liang³^ORCID,He Lang⁴^ORCID,Lin Ying⁵

Affiliation:

1. College of Mechanical and Electrical Engineering, Xidian University, Xi’an, China

2. College of School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China

3. School of Computer Science, University of Guelph, Guelph, ON, Canada

4. School of Computer Science, Xi’an University of Posts and Telecommunications, Xi’an, China

5. Shaanxi Hanlin Holdings Group co. ltd., Xi’an, China

Funder

National Natural Science Foundation of China

Key Research and Development Projects of Shaanxi Province

Special Project of Shaanxi Provincial Department of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10607832/10517963.pdf?arnumber=10517963

Reference54 articles.

1. CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

2. Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

3. Federated Inverse Reinforcement Learning for Smart ICUs With Differential Privacy

4. Federated Learning: Challenges, Methods, and Future Directions

5. Deep Leakage from Gradients

Cited by 1 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. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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