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publications

Fed-NAD: Backdoor Resilient Federated Learning via Neural Attention Distillation

Published in Journal 1, 2024

The contents above will be part of a list of publications, if the user clicks the link for the publication than the contents of section will be rendered as a full page, allowing you to provide more information about the paper for the reader. When publications are displayed as a single page, the contents of the above “citation” field will automatically be included below this section in a smaller font.

Recommended citation: H. Ma, S. Qi, J. Yao, Y. Yuan, Y. Zou and D. Yu, "Fed-NAD: Backdoor Resilient Federated Learning via Neural Attention Distillation," 2024 10th IEEE International Conference on Intelligent Data and Security (IDS), NYC, NY, USA, 2024, pp. 7-13, doi: 10.1109/IDS62739.2024.00009. keywords: {Training;Data privacy;Toxicology;Federated learning;Distributed databases;Distance measurement;Data models;Federated Learning;Backdoor Attack;Neural Attention Distillation}, [http://academicpages.github.io/files/paper1.pdf](https://ieeexplore.ieee.org/document/10590622)

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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