Game-Theoretic Design of Quality-Aware Incentive Mechanisms for Hierarchical Federated Learning-Reference-Cited by-同舟云学术

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Game-Theoretic Design of Quality-Aware Incentive Mechanisms for Hierarchical Federated Learning

Published:2024-08-01 Issue:15 Volume:11 Page:26033-26045
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Hu Gangqiang¹^ORCID,Han Jianmin¹^ORCID,Lu Jianfeng²^ORCID,Yu Juan¹^ORCID,Qiu Sheng¹,Peng Hao¹,Zhu Donglin¹,Li Taiyong³^ORCID

Affiliation:

1. School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China

2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

3. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Key Research and Development Program of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10607832/10509588.pdf?arnumber=10509588

Reference46 articles.

1. A Comprehensive Survey on Blockchain in Industrial Internet of Things: Motivations, Research Progresses, and Future Challenges

2. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

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

4. Fuzzing: A Survey for Roadmap

5. Toward Fairness-Aware Time-Sensitive Asynchronous Federated Learning for Critical Energy Infrastructure

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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