Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory

Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory

With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering perfo...

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Bibliographic Details
Main Authors: Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Self-organizing feature maps
adaptive resonance theory
continual learning
federated clustering
local ϵ-differential privacy
Online Access:https://ieeexplore.ieee.org/document/10689590/
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