Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a lack of work considering DP and its implications for cross …

Adaptive optimization methods have become the default solvers for many machine learning
tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential …

This paper studies federated learning (FL)--especially cross-silo FL--with data from people
who do not trust the server or other silos. In this setting, each silo (eg hospital) has data from …

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by
any algorithm. Traditional formulations impose a uniform privacy requirement for all users …

In recent years, machine learning (ML) systems have increasingly been deployed in
industry, government, and society. Although ML models can be extremely useful and …

Developing machine learning models heavily relies on access to data. To build a
responsible data economy and protect data ownership, it is crucial to enable learning …

While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a relative lack of work considering DP and its implications …

The increasingly distributed nature of many current and future technologies has introduced
many challenges for devices designed for such settings. Devices operating in such …