Toward Securing Federated Learning Against Poisoning Attacks in Zero Touch B5G Networks
Author:
Affiliation:
1. Communication Systems Department, EURECOM, Sophia Antipolis, France
2. DRIVE EA1859, University of Bourgogne, Nevers, France
Funder
European Union’s H2020 MonB5G
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/4275028/10168539/10130620.pdf?arnumber=10130620
Reference20 articles.
1. Certified defenses for data poisoning attacks;steinhardt;Proc NeurIPS,2017
2. PDGAN: A novel poisoning defense method in federated learning using generative adversarial network;zhao;Proc ICA3PP,2019
3. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
4. Deep probabilistic models to detect data poisoning attacks;subedar;arXiv 1912 01206,2019
5. On the feasibility of Federated Learning towards on-demand client deployment at the edge
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