Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning-Reference-Cited by-同舟云学术

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Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning

Published:2023 Issue: Volume:18 Page:1839-1854
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Liu Ziyao¹^ORCID,Guo Jiale¹^ORCID,Lam Kwok-Yan¹^ORCID,Zhao Jun¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

Funder

National Research Foundation, Singapore, under its Strategic Capability Research Centres Funding Initiative

Singapore Ministry of Education Academic Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/09757847.pdf?arnumber=9757847

Reference61 articles.

1. Local Differential Privacy-Based Federated Learning for Internet of Things

2. Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System

3. Local differential privacy and its applications: A comprehensive survey;yang;arXiv 2008 03686,2020

4. How to backdoor federated learning;bagdasaryan;Proc Int Conf Artif Intell Statist,2020

5. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

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2. HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems;IEEE Transactions on Green Communications and Networking;2024-09

3. Efficient FHE-Based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service;IEEE Transactions on Dependable and Secure Computing;2024-09

4. TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning;IEEE Transactions on Dependable and Secure Computing;2024-09

5. On the Information Theoretic Secure Aggregation With Uncoded Groupwise Keys;IEEE Transactions on Information Theory;2024-09

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