FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things-Reference-Cited by-同舟云学术

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FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things

Published:2022-03 Issue:1 Volume:5 Page:85-90
ISSN:2576-3180
Container-title:IEEE Internet of Things Magazine
language:
Short-container-title:IEEE Internet Things M.

Author:

Qu Youyang¹,Xu Chenhao²,Gao Longxiang³,Xiang Yong²,Yu Shui⁴

Affiliation:

1. Data61, Commonwealth Scientific and Industrial Research Organization,Australia

2. Deakin University,Australia

3. Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan),China

4. University Technology Sydney,Australia

Funder

ARC

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/8548628/9773082/09773120.pdf?arnumber=9773120

Reference15 articles.

1. A Secure Federated Learning Framework for 5G Networks

2. signsgd: Compressed Optimisation for Non-Convex Problems;bernstein;Int'l Conf Machine Learning,2018

3. Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning

4. Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures;cui;IEEE Trans Industrial Informatics,2021

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2. Defending Against Poisoning Attacks in Federated Learning With Blockchain;IEEE Transactions on Artificial Intelligence;2024-07

3. An Efficient Privacy-Preserving Machine Learning for Blockchain Network;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

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

5. Fedstellar: A Platform for Decentralized Federated Learning;Expert Systems with Applications;2024-05

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