arXiv Machine Learning
Aug 18
Stitch the Fragments: One-Shot Hierarchical Federated Clustering
arXiv:2601. 06404v2 Announce Type: replace Abstract: Federated Clustering (FC) faces a critical bottleneck in real-world scenarios, i.
Evolving Federated Density Peak Clustering with Shuffler-Based Anonymization
The paper introduces Evolving Federated Density Peak Clustering (eFDPC), a one‑shot federated clustering method that merges evolving fuzzy data‑stream clustering with density peak clustering. Each client independently updates micro‑clusters from private mini‑batches, and only selected density peaks are sent to the server, reducing communication. A shuffler‑based anonymization layer protects client contributions, and two variants—eFDPC‑P and eFDPC‑G—are evaluated on synthetic and real datasets, outperforming recent federated clustering baselines in one‑shot settings and handling high‑dimensional, non‑IID data.
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arXiv Machine Learning
Oct 3
StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation
StoCFL is a clustered federated learning framework designed to address Non-IID data and dynamic client participation. It introduces a flexible clustering mechanism that allows arbitrary client participation and accommodates newly joined clients, improving data efficiency and model performance. Experiments on four Non-IID settings and a real-world dataset demonstrate that StoCFL achieves promising cluster results even when the number of clusters is unknown, outperforming baseline approaches across various scenarios.
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).
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.
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.
arXiv Machine Learning
Jul 31
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
arXiv:2607. 28338v1 Announce Type: new Abstract: Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effective training.
arXiv Machine Learning
Oct 7
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Fed-BRDECS is a federated deep embedded clustering framework that protects client privacy by replacing globally normalized clustering objectives with a locally computable sample‑stability loss, eliminating the need to share sensitive soft‑assignment distributions. It addresses non‑IID client data through prediction‑balanced sampling, which oversamples rare predicted clusters, and centroid‑level restarting, which refreshes biased or inactive centroids. Experiments on image, text, and time‑series datasets demonstrate that Fed‑BRDECS outperforms existing federated and deep clustering methods while adding no inference‑time cost for anomaly detection.
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.
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.
arXiv Machine Learning
Jul 14
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
arXiv:2605. 11165v3 Announce Type: replace Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.
Hugging Face Trending Papers
Aug 10
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.
arXiv AI
Aug 11
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
arXiv:2608. 09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy.