Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering

• URL: http://arxiv.org/abs/2509.16101v1
• Date: Fri, 19 Sep 2025 15:45:02 GMT
• Title: Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
• Authors: Kristina P. Sinaga,
• Abstract summary: We present a robust personalized learning framework for multi-view fuzzy c-means clustering.<n>Our approach integrates heat- kernel coefficients adapted from quantum field theory with Tucker decomposition and canonical polyadic decomposition.
• Score: 2.538209532048867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a robust personalized federated learning framework that leverages heat-kernel enhanced tensorized multi-view fuzzy c-means clustering with advanced tensor decomposition techniques. Our approach integrates heat-kernel coefficients adapted from quantum field theory with Tucker decomposition and canonical polyadic decomposition (CANDECOMP/PARAFAC) to transform conventional distance metrics and efficiently represent high-dimensional multi-view structures. The framework employs matriculation and vectorization techniques to facilitate the discovery of hidden structures and multilinear relationships via N-way generalized tensors. The proposed method introduces a dual-level optimization scheme: local heat-kernel enhanced fuzzy clustering with tensor decomposition operating on order-N input tensors, and federated aggregation of tensor factors with privacy-preserving personalization mechanisms. The local stage employs tensorized kernel Euclidean distance transformations and Tucker decomposition to discover client-specific patterns in multi-view tensor data, while the global aggregation process coordinates tensor factors (core tensors and factor matrices) across clients through differential privacy-preserving protocols. This tensorized approach enables efficient handling of high-dimensional multi-view data with significant communication savings through low-rank tensor approximations.

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