Cooperative training methods for distributed machine learning typically assume noiseless
and ideal communication channels. This work studies some of the opportunities and …

Cooperative training methods for distributed machine learning are typically based on the
exchange of local gradients or local model parameters. The latter approach is known as …

Traditional deep learning models are trained on centralized servers using labeled sample
data collected from edge devices. This data often includes private information, which the …

In this work, we study the problem of federated learning (FL), where distributed users aim to
jointly train a machine learning model with the help of a parameter server (PS). In each …

Communication constraints are one of the major challenges preventing the wide-spread
adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …

In federated learning, all networked clients contribute to the model training cooperatively.
However, with model sizes increasing, even sharing the trained partial models often leads to …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

Communication constraints are one of the majorchallenges preventing the wide-spread
adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new …

The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …