Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

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 …

ChatGPT, the latest text-based artificial intelligence (AI) tool, has quickly gained popularity
and is poised to revolutionize various aspects of our lives, including education and research …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3 (. 5) and
GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT …

Instruction tuning is an effective technique to align large language models (LLMs) with
human intent. In this work, we investigate how an adversary can exploit instruction tuning by …

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

Fairness and robustness are two important concerns for federated learning systems. In this
work, we identify that robustness to data and model poisoning attacks and fairness …

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 a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …