aiqbench analysis
This paper addresses a critical gap in unsupervised learning by proposing FedDCN, a federated deep clustering framework designed to handle non-IID data distributions across private clients. By combining synthetic data augmentations with geometric regularization for latent space alignment, the method improves robustness where existing approaches often fail under heterogeneous conditions. The work advances the state of privacy-preserving machine learning, offering a viable path toward scalable clustering in distributed environments without centralizing sensitive datasets.
Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data
arXiv:2609.21829v1 Announce Type: cross Abstract: Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized data scenario, this task is commonly solved using deep clustering methods that utilize deep neural network architectures to learn clustering-friendly latent space representations. In Federated Learning, where data is distributed between clients and is private, deep clustering methods are less explored. In particular, recently introduced federated deep clustering methods, despite showing very promising performance, still fall short in reliably providing good performance if data across clients are non-identically-independently distributed. In this work, we introduce a generalization of Deep Clustering Networks to the federated scenario, named FedDCN, that simultaneously optimizes a reconstruction loss and a clustering loss. To ensure robustness and latent space alignment in non-identically-independently distributed data scenarios, FedDCN generates synthetic data augmentations, and its learning objective includes a geometric regularization for latent space alignment. Through experimental evaluation, the effectiveness of the approach under IID and non-IID assumptions is demonstrated, and future research directions are identified.
Abstract:Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized data scenario, this task is commonly solved using deep clustering methods that utilize deep neural network architectures to learn clustering-friendly latent space representations. In Federated Learning, where data is distributed between clients and is private, deep clustering methods are less explored. In particular, recently introduced federated deep clustering methods, despite showing very promising performance, still fall short in reliably providing good performance if data across clients are non-identically-independently distributed. In this work, we introduce a generalization of Deep Clustering Networks to the federated scenario, named FedDCN, that simultaneously optimizes a reconstruction loss and a clustering loss. To ensure robustness and latent space alignment in non-identically-independently distributed data scenarios, FedDCN generates synthetic data augmentations, and its learning objective includes a geometric regularization for latent space alignment. Through experimental evaluation, the effectiveness of the approach under IID and non-IID assumptions is demonstrated, and future research directions are identified.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.21829 [cs.LG]
(or arXiv:2609.21829v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.21829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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