Evolving Topics in Federated Learning: Trends, and Emerging Directions for IS

TL;DR

This paper provides a comprehensive review of federated learning, analyzing current trends, key research topics, and future directions to guide IS researchers and practitioners in understanding and advancing the field.

Contribution

It offers a detailed computational literature review of federated learning, identifying 15 key research topics and proposing future research questions for IS scholars.

Findings

Identified 15 prominent research topics in federated learning.

Analyzed trends and influential areas in FL research.

Proposed guiding questions for future IS research in FL.

Abstract

Federated learning (FL) is a popular approach that enables organizations to train machine learning models without compromising data privacy and security. As the field of FL continues to grow, it is crucial to have a thorough understanding of the topic, current trends and future research directions for information systems (IS) researchers. Consequently, this paper conducts a comprehensive computational literature review on FL and presents the research landscape. By utilizing advanced data analytics and leveraging the topic modeling approach, we identified and analyzed the most prominent 15 topics and areas that have influenced the research on FL. We also proposed guiding research questions to stimulate further research directions for IS scholars. Our work is valuable for scholars, practitioners, and policymakers since it offers a comprehensive overview of state-of-the-art research on FL.