arXiv Machine Learning

Federated Soft Clustering via Generalized Total Variation Minimization

The paper introduces federated soft clustering for devices in a federated learning network, each fitting a personalized Gaussian mixture model. It proposes Generalized Total Variation Minimization (GTVMin) to couple local maximum likelihood problems via a graph regularizer that penalizes discrepancies between connected nodes’ models. Three discrepancy measures are compared: a squared Euclidean distance requiring component matching, a Monte‑Carlo approximated Kullback‑Leibler divergence, and a closed‑form maximum mean discrepancy; all are optimized with synchronous projected gradient updates, with a convergence guarantee for the smooth MMD instance.

arXiv Machine Learning

Oct 15

A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization

The paper presents a large‑dimensional analysis of Generalized Total Variation Minimization (GTVMin) for least‑squares federated learning. It shows that Lipschitz observations of the global GTVMin solution concentrate in high dimensions regardless of network structure, and derives exact analytic expressions for performance under both i.i.d. and non‑i.i.d. Gaussian mixture models. These results quantify how regularization balances local adaptation against collaboration, and are validated by experiments on synthetic and real‑world data.

By Ekkehard Schnoor, Malik Tiomoko, Alexander Jung

safety

arXiv Machine Learning

Jun 2

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering

arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.

By Xu Zhang, Wenpeng Li, Yunfeng Shao, Yonglin Liu, Kaiwen Zhou, Yinchuan Li

efficiency safety

arXiv Machine Learning

Jun 19

Variational Consensus Monte Carlo for Bayesian Mixture

arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.

By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk

benchmarks safety

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 Machine Learning

Oct 21

Federated Learning: From Theory to Practice

arXiv:2505.19183v4 Announce Type: replace Abstract: This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smar...

By A. Jung

safety

arXiv Machine Learning

Jun 24

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

arXiv:2509. 22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions.

By Sre\'cko {\DJ}ura\v{s}inovi\'c, Jean-Bernard Lasserre, Victor Magron

arXiv Statistics ML

Sep 10

Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts

arXiv:1907.06994v2 Announce Type: replace-cross Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...

By Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van, Bao Tuyen Huynh

efficiency benchmarks

arXiv Machine Learning

Oct 5

RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

RIPPLE is a clustered federated learning framework that assigns clients to groups based on an offline spectral characterization of their local data. It uses a variance‑weighted principal‑component prototype embedded via the Wavelet Scattering Transform and decoded by a Gaussian Mixture VAE trained on synthetic client populations. The method achieves the same per‑round communication cost as FedAvg, allows absent clients to receive a personalized model with a single forward pass, and demonstrates superior performance across five heterogeneous benchmarks.

By Alessandro Licciardi

benchmarks

arXiv Machine Learning

Jun 8

Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.

By Su Wang, Mung Chiang, H. Vincent Poor

arXiv Machine Learning

Jun 9

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps

arXiv:2510. 12744v2 Announce Type: replace-cross Abstract: We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model selection: (i) non-identifiability of gating parameters up to common translations, (ii) intrinsic gate-expert interactions that induce coupled differential relations in the likelihood, and (iii) the tight numerator-denominator coupling in the softmax-induced conditional density.

By Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen, Nhat Ho, Binh T. Nguyen, Christopher Drovandi

arXiv Statistics ML

Sep 2

Deep Skew-t Mixture Models

arXiv:2609.00773v1 Announce Type: cross Abstract: High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew-$t...

By Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan

benchmarks

arXiv AI

Sep 21

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.

By Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik, Anna Wilbik

diffusion safety

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