Private Knowledge Sharing in Distributed Learning: A Survey
Yasas Supeksala, Dinh C. Nguyen, Ming Ding, Thilina Ranbaduge, Calson, Chua, Jun Zhang, Jun Li, H. Vincent Poor

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…
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Taxonomy
TopicsCryptography and Data Security · Access Control and Trust · Cooperative Communication and Network Coding
