The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Although the fifth generation (5G) wireless networks are yet to be fully investigated, the
visionaries of the 6th generation (6G) echo systems have already come into the discussion …

In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …

Abstract Machine learning are vulnerable to the threats. The Intruders can utilize the
malicious nature of the nodes to attack the training dataset to worsen the process and …

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the
rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets …

In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …

Federated learning distributes model training among a multitude of agents, who, guided by
privacy concerns, perform training using their local data but share only model parameter …