Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

Emerging applications---cloud computing, the internet of things, and augmented/virtual
reality---demand responsive, secure, and scalable datacenter networks. These networks …

Facebook takes performance monitoring seriously. Performance issues can impact over one
billion users so we track thousands of servers, hundreds of PB of daily network traffic …

Hadoop and Spark are widely used distributed processing frameworks for large-scale data
processing in an efficient and fault-tolerant manner on private or public clouds. These big …

Timely and cost-effective analytics over" big data" has emerged as a key ingredient for
success in many businesses, scientific and engineering disciplines, and government …

Big data has become part of the life for many people. The data about people's life are being
continously collected, analysized and applied as our society progresses into the big data …

The MapReduce programming model simplifies large-scale data processing on commodity
cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been …

Hierarchical clustering has been widely used in numerous applications due to its informative
representation of clustering results. But its higher computation cost and inherent data …