Poisoning Attacks against Federated Learning in Load Forecasting of Smart Energy-Reference-Cited by-同舟云学术

Poisoning Attacks against Federated Learning in Load Forecasting of Smart Energy

Published:2022-04-25 Issue: Volume: Page:
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Container-title:NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
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Author:

Qureshi Naik Bakht Sania¹,Kim Dong-Hoon¹,Lee Jiwoo¹,Lee Eun-Kyu¹

Affiliation:

1. Incheon National University,Dept. of Information and Telecommunication Engineering,Incheon,Republic of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9789703/9789704/09789884.pdf?arnumber=9789884

Reference27 articles.

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

2. Data Poisoning against Differentially-Private Learners: Attacks and Defenses

3. How to backdoor federated learning’;bagdasaryan;International Conference on Artificial Intelligence and Statistics,2020

4. Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks;zhao;Concurrency and Computation Practice and Experience,2020

5. Performance Analysis of Distributed and Federated Learning Models on Private Data

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