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 …

Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

We present a novel approach to federated learning that endows its aggregation process with
greater robustness to potential poisoning of local data or model parameters of participating …

We train language models (LMs) with federated learning (FL) and differential privacy (DP) in
the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP …

We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy
mechanism based on the difference of two independent Poisson random variables. To …

A key tool for building differentially private systems is adding Gaussian noise to the output of
a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …