Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data

2026-09-18

AI Summary

本文提出FedDCN方法，通过优化重构损失和聚类损失，并生成合成数据增强及几何正则化来解决联邦学习中高维异构数据的聚类问题。

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.

Problem

• Federated Learning
• Deep Clustering
• Non-IID Data

Innovation

• Federated Deep Clustering
• Latent Space Alignment
• Geometric Regularization
• Synthetic Data Augmentation
• Non-IID Data