Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Deep learning technology has developed unprecedentedly in the last decade and has
become the primary choice in many application domains. This progress is mainly attributed …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated
learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to …

In this paper, we explore learning statistically consistent classifiers under label noise by
estimating the noise transition matrix T. We first provide a holistic view of existing T …

Dataset Distillation is used to create a concise, yet informative, synthetic dataset that can
replace the original dataset for training purposes. Some leading methods in this domain …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Model poisoning attacks are critical security threats to Federated Learning (FL). Existing
model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal …

There is a rising interest in further exploring the zero-shot learning potential of large pre-
trained language models (PLMs). A new paradigm called data-generation-based zero-shot …

Blackbox optimization problems are commonly seen in the real world, ranging from
experimental design to hyperparameter tuning of machine learning models. In numerous …