Privacy-Preserving Transfer Learning for Community Detection in Multiple Networks: A Review.

Abstract

In order to identify communities in various networks, this study gives a thorough analysis of privacy-preserving transfer learning methods.  In order to better understand the specific difficulties of implementing transfer learning in decentralized and diverse settings, it classifies current solutions according to their learning paradigms, privacy measures, and network topologies.  The scalability, privacy, and utility trade-offs are used to assess anonymization, deep learning, and federated learning methods.  There is a critical discussion of the gaps in the present research, including the absence of defined assessment standards and the inadequate incorporation of privacy into transfer systems.  Also, this research points the way toward potential future possibilities for developing privacy-first models that can generalize across different types of networks.  Researchers and practitioners in the field of graph-based machine learning may use the results as a guide to create safe and efficient solutions.