To cope with the lack of on-device machine learning samples, this article presents a
distributed data augmentation algorithm, coined federated data augmentation (FAug). In …

On-device machine learning (ML) enables the training process to exploit a massive amount
of user-generated private data samples. To enjoy this benefit, inter-device communication …

On-device machine learning (ML) has brought about the accessibility to a tremendous
amount of data from the users while keeping their local data private instead of storing it in a …

Over-the-air federated edge learning (Air-FEEL) shows promise as a distributed machine
learning paradigm for edge devices. By leveraging the superposition property of a multiple …

On-device learning is a promising technique for emerging privacy-preserving machine
learning paradigms. However, through quantitative experiments, we find that commodity …

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the
edge. Deep learning models deployed on edge devices are required to learn from these …

Data privacy preservation has drawn much attention with emerging machine learning
applications. Federated Learning is thus developed to offer decentralized learning on user …

Federated learning (FL) allows edge devices to collectively learn a model without directly
sharing data within each device, thus preserving privacy and eliminating the need to store …

Federated learning (FL) is an attractive distributed machine learning framework due to the
property of privacy preservation. The implementation of FL encounters the challenge of the …

Decentralized training of deep learning models is a key element for enabling data privacy
and on-device learning over networks. In realistic learning scenarios, the presence of …