Private Knowledge Sharing in Distributed Learning: A Survey

TL;DR

This survey reviews methods and challenges of private knowledge sharing in distributed learning, focusing on vulnerabilities, privacy-preserving strategies, and future research directions in decentralized AI systems.

Contribution

It provides a comprehensive overview of privacy issues, defenses, and limitations in distributed learning architectures, highlighting areas for future investigation.

Findings

Identifies key vulnerabilities in distributed knowledge sharing.

Analyzes privacy-preserving defense strategies.

Discusses limitations and future research directions.

Abstract

The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed the way society operates. Its widespread use has led to the distribution of AI and its underlying data across many intelligent systems. In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities. As a result, modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes. In line with this goal, the latest AI models are frequently trained in a decentralized manner. Distributed learning involves multiple entities working together to make collective predictions and decisions. However, this collaboration can also bring about security vulnerabilities and challenges. This paper provides an in-depth survey on private knowledge sharing in distributed learning, examining various knowledge components utilized in leading distributed learning architectures. Our analysis sheds light on the most critical vulnerabilities that may arise when using these components in a distributed setting. We further identify and examine defensive strategies for preserving the privacy of these knowledge components and preventing malicious parties from manipulating or accessing the knowledge information. Finally, we highlight several key limitations of knowledge sharing in distributed learning and explore potential avenues for future research.