Sandbox Computing: A Data Privacy Trusted Sharing Paradigm via Blockchain and Federated Learning-Reference-Cited by-同舟云学术

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Sandbox Computing: A Data Privacy Trusted Sharing Paradigm via Blockchain and Federated Learning

Published:2022 Issue: Volume: Page:1-12
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Guo Shaoyong¹^ORCID,Zhang Keqin¹^ORCID,Gong Bei²^ORCID,Chen Liandong³,Ren Yinlin¹^ORCID,Qi Feng¹^ORCID,Qiu Xuesong¹^ORCID

Affiliation:

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

2. Beijing University of Technology, Beijing, China

3. Information and Telecommunication Branch, State Grid Hebei Electric Power Company Ltd,, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/4358213/09791849.pdf?arnumber=9791849

Reference30 articles.

1. Client-Edge-Cloud Hierarchical Federated Learning

2. iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks

3. Energy-Efficient Radio Resource Allocation for Federated Edge Learning

4. EdgeFed: Optimized Federated Learning Based on Edge Computing

5. Asynchronous methods for deep reinforcement learning;mnih;Proc 33rd Int Conf Int Conf Mach Learn,2016

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2. Integrating Federated Learning for Privacy Protection in Intelligent Home Care Robots;2024 International Symposium on Intelligent Robotics and Systems (ISoIRS);2024-06-14

3. An Optimized FL-XAI model for secured and trustworthy candidate selection;2024-06-10

4. Privacy-preserving in Blockchain-based Federated Learning systems;Computer Communications;2024-06

5. Verifiable Federated Learning Based on Data Service Quality;2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS);2024-05-31

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