A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing-Reference-Cited by-同舟云学术

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing

Published:2023-12-15 Issue:24 Volume:10 Page:21942-21958
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Moore Ervin¹,Imteaj Ahmed²^ORCID,Rezapour Shabnam³,Amini M. Hadi⁴^ORCID

Affiliation:

1. Knight Foundation School of Computing and Information Sciences, and the Sustainability, Optimization, and Learning for Interdependent Networks Laboratory, Florida International University, Miami, FL, USA

2. Security, Privacy and Edge Intelligence for Distributed Networks Laboratory, Southern Illinois University, Carbondale, IL, USA

3. Smart Decision-Making for Network-Centric Systems Laboratory, Florida International University, Miami, FL, USA

4. Knight Foundation School of Computing and Information Sciences, the Sustainability, Optimization, Learning for Interdependent Networks Laboratory (solid lab), and the Advanced Education and Research for Machine Learning-Driven Critical Infrastructure Resilience (ADMIRE) Center, Florida International University, Miami, FL, USA

Funder

U.S. Department of Homeland Security

US Department of Transportation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10353047/10242386.pdf?arnumber=10242386

Reference103 articles.

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

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

3. Advances and Open Problems in Federated Learning

4. A Survey on Federated Learning for Resource-Constrained IoT Devices

5. Federated Learning With Differential Privacy: Algorithms and Performance Analysis

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1. Survey on Blockchain-Enhanced Machine Learning;IEEE Access;2023