In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

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 …

We introduce Opacus, a free, open-source PyTorch library for training deep learning models
with differential privacy (hosted at opacus. ai). Opacus is designed for simplicity, flexibility …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …

Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Trusted execution environments (TEEs) see rising use in devices from embedded sensors to
cloud servers and encompass a range of cost, power constraints, and security threat model …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …