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 …
T Li, M Zaheer, S Reddi… - … Conference on Machine …, 2022 - proceedings.mlr.press
Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential …
A Lowy, M Razaviyayn - arXiv preprint arXiv:2106.09779, 2021 - arxiv.org
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 …