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 …

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 …

With privacy as a motivation, Federated Learning (FL) is an increasingly used paradigm
where learning takes place collectively on edge devices, each with a cache of user …

Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …

Federated learning is used for decentralized training of machine learning models on a large
number (millions) of edge mobile devices. It is challenging because mobile devices often …

Federated learning (FL) allows clients in an Internet of Things (IoT) system to collaboratively
train a global model without sharing their local data with a server. However, clients' …

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 …

Federated Learning using the Federated Averaging algorithm has shown great advantages
for large-scale applications that rely on collaborative learning, especially when the training …

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 …

Federated learning alleviates the privacy risk in distributed learning by transmitting only the
local model updates to the central server. However, it faces challenges including statistical …