Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

The richness of social media data has opened a new avenue for social science research to
gain insights into human behaviors and experiences. In particular, emerging data-driven …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

Creating and maintaining a platform for reliably producing and deploying machine learning
models requires careful orchestration of many components---a learner for generating …

Codes are widely used in many engineering applications to offer robustness against noise.
In large-scale systems, there are several types of noise that can affect the performance of …

Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

We propose a parameter server framework for distributed machine learning problems. Both
data and workloads are distributed over worker nodes, while the server nodes maintain …