Machine learning benefits from large training datasets, which may not always be possible to
collect by any single entity, especially when using privacy-sensitive data. In many contexts …

In large-scale statistical learning, data collection and model fitting are moving increasingly
toward peripheral devices---phones, watches, fitness trackers---away from centralized data …

In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …

Deep learning has achieved great success in many artificial intelligence applications such
as healthcare systems, recommendation systems, and network security. However, the …

Federated Learning allows distributed entities to train a common model collaboratively
without sharing their own data. Although it prevents data collection and aggregation by …

Federated Learning (FL) is a promising machine learning paradigm that enables the
analyzer to train a model without collecting users' raw data. To ensure users' privacy …

We propose new techniques for reducing communication in private federated learning
without the need for setting or tuning compression rates. Our on-the-fly methods …

Extracting useful knowledge from big data is important for machine learning. When data is
privacy-sensitive and cannot be directly collected, federated learning is a promising option …

Collaborative learning enables two or more participants, each with their own training
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …

Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …