Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning-Reference-Cited by-同舟云学术

Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Smart Computing (SMARTCOMP)
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Author:

Shahid Abdur R.¹,Imteaj Ahmed²,Badsha Shahriar³,Hossain Md Zarif²

Affiliation:

1. Robert Morris University,Department of Computer and Information Systems,PA,USA

2. Southern Illinois University,School of Computing,Carbondale,USA,IL 62901

3. Bosch Engineering North America,Farmington Hills,MI,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10207555/10207557/10207617.pdf?arnumber=10207617

Reference26 articles.

1. Analyzing federated learning through an adversarial lens;bhagoji;International Conference on Machine Learning,2019

2. Label sanitization against label flipping poisoning attacks;paudice;Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases,2018

3. Privacy and Robustness in Federated Learning: Attacks and Defenses

4. Data poisoning attacks on federated machine learning;sun;IEEE Internet of Things Journal,2021

5. Human Motion Recognition Using Zero-Shot Learning

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