发表机构
Maastricht University（马斯特里赫特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦学习中高维异构数据聚类问题，提出FedDCN方法，通过联合优化重建与聚类损失并引入几何正则化，在非独立同分布场景下实现鲁棒聚类。
AI 中文摘要
对高维数据进行聚类是无监督机器学习中的一项基本任务，其应用涉及多个领域。在集中式数据场景中，该任务通常通过深度聚类方法来解决，这些方法利用深度神经网络架构来学习有利于聚类的潜在空间表示。在联邦学习中，数据分布在各个客户端之间且具有隐私性，深度聚类方法的探索相对较少。特别是，最近提出的联邦深度聚类方法尽管展现出非常有前景的性能，但在客户端数据非独立同分布的情况下，仍未能可靠地提供良好的性能。在这项工作中，我们将深度聚类网络推广到联邦场景，提出了一种名为FedDCN的方法，该方法同时优化重建损失和聚类损失。为了在非独立同分布的数据场景中确保鲁棒性和潜在空间对齐，FedDCN生成合成数据增强，其学习目标包含用于潜在空间对齐的几何正则化。通过实验评估，我们证明了该方法在独立同分布和非独立同分布假设下的有效性，并指出了未来的研究方向。
英文摘要
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.
CommentsAccepted to the 4th International Conference on Federated Learning Technologies and Applications (FLTA 2026)
