Josh Gardner

TL;DR: In this article, the authors describe the state-of-the-art in the field of federated learning from the perspective of distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, and statistics.

TL;DR: Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

TL;DR: This work provides a framework for quantifying and understanding how predictive models might inadvertently privilege, or disparately impact, different student subgroups and suggests that learning analytics researchers and practitioners can use slicing analysis to improve model fairness without necessarily sacrificing performance.

TL;DR: In this paper, the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model.

TL;DR: This article presents a categorization of MOOC research according to the predictors, prediction, and underlying theoretical model, and critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments.