Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …

With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

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 …

When trying to gain better visibility into a machine learning model in order to understand and
mitigate the associated risks, a potentially valuable source of evidence is: which training …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

The adversarial machine learning literature is largely partitioned into evasion attacks on
testing data and poisoning attacks on training data. In this work, we show that adversarial …

Federated Learning (FL) allows multiple participants to train machine learning models
collaboratively by keeping their datasets local while only exchanging model updates. Alas …

Data poisoning and backdoor attacks manipulate training data in order to cause models to
fail during inference. A recent survey of industry practitioners found that data poisoning is the …

Federated learning is a novel distributed learning framework, which enables thousands of
participants to collaboratively construct a deep learning model. In order to protect …