Cover Image

One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zuziak, Maciej Krzysztof, Pellungrini, Roberto, Rinzivillo, Salvatore
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

One-Shot Clustering for Federated Learning
by: Zuziak, Maciej Krzysztof, et al.
Published: (2025)

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
by: Mestari, Soumia Zohra El, et al.
Published: (2025)

Energy-Efficient Federated Learning for AIoT using Clustering Methods
by: Pereira, Roberto, et al.
Published: (2025)

Explainable AI in Time-Sensitive Scenarios: Prefetched Offline Explanation Model
by: Russo, Fabio Michele, et al.
Published: (2025)

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
by: Wang, Zheng, et al.
Published: (2023)

CCFC: Bridging Federated Clustering and Contrastive Learning
by: Liu, Jing, et al.
Published: (2024)

GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
by: Fragkathoulas, Christos, et al.
Published: (2026)

Capture Global Feature Statistics for One-Shot Federated Learning
by: Guan, Zenghao, et al.
Published: (2025)

Equitable Federated Learning with Activation Clustering
by: Upadhyay, Antesh, et al.
Published: (2024)

Neural Cluster First, Route Second: One-Shot Capacitated Vehicle Routing via Differentiable Optimal Transport
by: Chin, Samuel J. K., et al.
Published: (2026)

Generalization in Kernel Regression Under Realistic Assumptions
by: Barzilai, Daniel, et al.
Published: (2023)

Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints
by: Sysoykova, Ekaterina, et al.
Published: (2025)

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
by: Ranaweera, Kanishka, et al.
Published: (2025)

AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
by: Punzi, Clara, et al.
Published: (2024)

Differentially Private Federated Clustering with Random Rebalancing
by: Yang, Xiyuan, et al.
Published: (2025)

CLAMS: A System for Zero-Shot Model Selection for Clustering
by: Singh, Prabhant, et al.
Published: (2024)

FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
by: Moleri, Andrea, et al.
Published: (2026)

Interpretable and Fair Mechanisms for Abstaining Classifiers
by: Lenders, Daphne, et al.
Published: (2025)

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
by: Bhatia, Randeep, et al.
Published: (2025)

MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
by: Fang, Kai, et al.
Published: (2025)

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
by: Ye, Wenxuan, et al.
Published: (2025)

The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
by: Turazza, Fabio, et al.
Published: (2026)

Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning
by: Rostami, Mohammad, et al.
Published: (2024)

One-Shot Federated Learning with Bayesian Pseudocoresets
by: d'Hondt, Tim, et al.
Published: (2024)

GeFL: Model-Agnostic Federated Learning with Generative Models
by: Kang, Honggu, et al.
Published: (2024)

A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew
by: Ali, Michael Ben, et al.
Published: (2025)

Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
by: Kritharakis, Emmanouil, et al.
Published: (2025)

Mathematical Foundation of Interpretable Equivariant Surrogate Models
by: Colombini, Jacopo Joy, et al.
Published: (2025)

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
by: Ciaperoni, Martino, et al.
Published: (2026)

On the Independence Assumption in Neurosymbolic Learning
by: van Krieken, Emile, et al.
Published: (2024)

Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model
by: Wang, Naibo, et al.
Published: (2025)

Break the Tie: Learning Cluster-Customized Category Relationships for Categorical Data Clustering
by: Zhao, Mingjie, et al.
Published: (2025)

Clustered Federated Learning with Hierarchical Knowledge Distillation
by: Ahmad, Sabtain, et al.
Published: (2025)

Semi-Supervised One-Shot Imitation Learning
by: Wu, Philipp, et al.
Published: (2024)

Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
by: Leiber, Collin, et al.
Published: (2024)

An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
by: Leiber, Collin, et al.
Published: (2025)

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
by: Chao, Guoqing, et al.
Published: (2025)

Towards plausibility in time series counterfactual explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)

FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
by: Fan, Boyu, et al.
Published: (2024)