Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data-Reference-Cited by-同舟云学术

Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

Published:2024-01-01 Issue:1 Volume:11 Page:1241-1255
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Shen Xicong¹^ORCID,Liu Ying¹^ORCID,Li Fu²,Li Chunguang¹^ORCID

Affiliation:

1. College of Information Science and Electronic Engineering and the Zhejiang Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou, China

2. Archives and the Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang, China

Funder

Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province

National Natural Science Foundation of China

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/ielx7/6488907/10375273/10160091.pdf?arnumber=10160091

Reference44 articles.

1. Recent Progress on the Convergence of the Internet of Things and Artificial Intelligence

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

3. A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things

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

5. Federated Machine Learning

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