arXiv AI

Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data

The paper introduces FedDCN, a federated deep clustering network that jointly optimizes reconstruction and clustering losses for high‑dimensional, heterogeneous data. It addresses challenges of non‑IID client data by generating synthetic augmentations and applying geometric regularization to align latent spaces. Experiments show the method’s effectiveness under both IID and non‑IID settings, and the authors outline future research directions.

arXiv Machine Learning

Aug 26

How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

The paper examines whether Deep Embedded Clustering (DEC) truly overcomes the fundamental limitations of k‑means clustering, such as handling clusters of arbitrary shapes, varied sizes, and densities. Through analysis, it finds that DEC does not exploit the underlying data distribution and therefore fails to address these limitations. Instead, a non‑deep learning approach that leverages distributional information of clusters can achieve the intended goals of deep clustering.

By Kai Ming Ting, Wei-Jie Xu, Hang Zhang

arXiv AI

Jun 16

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).

By Randeep Bhatia, Nikos Papadis, Murali Kodialam, TV Lakshman, Sayak Chakrabarty

rag benchmarks

arXiv Statistics ML

Aug 25

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures

arXiv:2403.14830v2 Announce Type: replace Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensio...

By Zeya Wang, Chenglong Ye

rag

arXiv Machine Learning

Sep 25

Selective Inference for Deep Clustering in Latent Spaces

The paper introduces a selective inference framework tailored for deep clustering that uses a fixed pretrained encoder to map high‑dimensional data into a latent space before clustering. It addresses the complex selection bias arising from the nonlinear transformation and offers a computationally tractable method to perform valid statistical tests on cluster differences. Experiments on synthetic data show controlled Type I error and higher power compared to conservative baselines, while genomic case studies demonstrate the ability to uncover significant cluster differences while properly accounting for selection bias.

By Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino, Ichiro Takeuchi

diffusion safety

arXiv Machine Learning

Jun 2

Closing the Alignment-Maturity Gap in Federated Prototype Learning

arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).

By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas

rag benchmarks safety

arXiv Machine Learning

Aug 4

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.

By Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan, Manar D. Samad

rag benchmarks safety

arXiv AI

Jun 4

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.

By Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim, Guillaume Rabusseau, Vladimir Makarenkov

benchmarks

arXiv AI

Aug 12

Sheaf-Based Federated Representation Learning

arXiv:2608. 10016v1 Announce Type: cross Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives.

By Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino, Mario Edoardo Pandolfo, Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo

rag agents diffusion safety

arXiv Computer Vision

4d ago

VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training

arXiv:2609.36648v1 Announce Type: new Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics...

By Yuanwei Hu, Bo Peng, Yuheng Jia, Xinting Hu, Yadan Luo, Wenjie Zhu

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 15

Federated Learning for Feature Generalization with Convex Constraints

arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.

By Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim

benchmarks

arXiv Machine Learning

Jun 30

Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning

arXiv:2606. 30499v1 Announce Type: new Abstract: Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions.

By Davide Domini, Gianluca Aguzzi, Ivana Dusparic, Danilo Pianini, Mirko Viroli

agents efficiency

arXiv Machine Learning

Jun 16

Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data

arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.

By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada

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