Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks

arXiv:2403.02983v1 Announce Type: new
Abstract: Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks.
In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficult …

arxiv attack attacks clients computer cs.ai cs.cr cs.cy cs.lg cs.ni data data poisoning decentralized devices edge exposing federated federated learning machine machine learning networks poisoning poisoning attacks servers sharing train training under updates vulnerabilities