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

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

Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence,
facilitating the parallel training of Artificial Neural Networks on edge devices while …

Abstract Deep Neural Networks (DNNs) usually have a large number of parameters and
consume a huge volume of storage space, which limits the application of DNNs on memory …

Recommender systems have become ubiquitous in the past years. They solve the tyranny of
choice problem faced by many users, and are utilized by many online businesses to drive …

This works explores the benefits of structured parameter pruning in the framework of the
MPEG standardization efforts for neural network compression. First less relevant parameters …

Abstract Recent advances in Industrial Internet of Things (IIoT) and communication
technologies have provided new concepts of smart manufacturing and paved the way for the …

The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way
for a new generation of robust and communication-efficient Federated Learning (FL), where …

Due to their great performance and scalability properties, deep neural networks have
become ubiquitous building blocks of many applications. With the rise of mobile and IoT …

Compact and efficient representations of deep neural networks Page 1 Compact and
efficient representations of deep neural networks vorgelegt von M. Sc. Simon Wiedemann …