Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

Deep Neural Network (DNN) models have continuously been growing in size in order to
improve the accuracy and quality of the models. Moreover, for training of large DNN models …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …

In this work, we present a federated version of the state-of-the-art Neural Collaborative
Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …