Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions

TL;DR

This paper reviews the integration of federated and split learning methods to enhance ubiquitous intelligence in IoT, highlighting recent advancements, challenges, and future research directions in edge computing environments.

Contribution

It provides a comprehensive survey of combining federated and split learning for IoT, identifying open problems and proposing future research directions.

Findings

Recent integration approaches improve privacy and efficiency in IoT learning

Open problems include scalability and security challenges

Future directions involve optimizing combined learning frameworks

Abstract

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with a hope to further arouse the research community's interest in this emerging field.