Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …

Secure multi-party computation (MPC) allows parties to perform computations on data while
keeping that data private. This capability has great potential for machine-learning …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …

As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …

We initiate the study of private inference on Transformer-based models in the client-server
setting, where clients have private inputs and servers hold proprietary models. Our main …

Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …