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

Apache Spark has emerged as the de facto framework for big data analytics with its
advanced in-memory programming model and upper-level libraries for scalable machine …

Passing gradient is a widely used scheme in modern multi-node learning system (eg,
distributed training, collaborative learning). In a long time, people used to believe that …

Federated learning (FL) is a promising approach for training decentralized data located on
local client devices while improving efficiency and privacy. However, the distribution and …

Distributed deep learning systems (DDLS) train deep neural network models by utilizing the
distributed resources of a cluster. Developers of DDLS are required to make many decisions …

With the explosive increase of big data in industry and academic fields, it is important to
apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state …

Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …

Recent studies have revealed severe privacy risks in federated learning represented by
Gradient Leakage Attacks. However existing studies mainly aim at increasing the privacy …

Many distributed deep learning systems have been published over the past few years, often
accompanied by impressive performance claims. In practice these figures are often …

Big data has reignited research interest in machine learning. Massive quantities of data are
being generated regularly as a consequence of the development in the Internet, social …