Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

Recent success in deep learning has partially been driven by training increasingly
overparametrized networks on ever larger datasets. It is therefore natural to ask: how much …

Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …

Scaling up the convolutional neural network (CNN) size (eg, width, depth, etc.) is known to
effectively improve model accuracy. However, the large model size impedes training on …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Abstract Knowledge distillation is a widely applicable technique for training a student neural
network under the guidance of a trained teacher network. For example, in neural network …