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 …

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to
displace many in the professional artist community. In particular, models can learn to mimic …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

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 …

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 …

With the success of deep learning algorithms in various domains, studying adversarial
attacks to secure deep models in real world applications has become an important research …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …