Sine: Similarity is Not Enough for Mitigating Local Model Poisoning Attacks in Federated Learning-Reference-Cited by-同舟云学术

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Sine: Similarity is Not Enough for Mitigating Local Model Poisoning Attacks in Federated Learning

Published:2024-09 Issue:5 Volume:21 Page:4481-4494
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Kasyap Harsh¹^ORCID,Tripathy Somanath¹^ORCID

Affiliation:

1. Indian Institute of Technology Patna, Daulatpur, Bihar, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8858/10663874/10398506.pdf?arnumber=10398506

Reference21 articles.

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

2. Federated learning for mobile keyboard prediction;Hard,2018

3. Towards federated learning at scale: System design;Bonawitz;Proc. Mach. Learn. Syst.,2019

4. Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

5. Machine learning with adversaries: Byzantine tolerant gradient descent;Blanchard

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network;Computer Networks;2024-12

2. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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