Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
This work addresses the challenge of efficient and privacy-preserving data analysis in federated learning for domains with complex multi-view structures, though it appears incremental as it builds on existing tensor and clustering methods.
The paper tackles the problem of handling high-dimensional multi-view data in federated learning by proposing a personalized framework that uses heat-kernel enhanced tensorized multi-view fuzzy c-means clustering with tensor decomposition, resulting in significant communication savings through low-rank approximations.
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.