arXiv Machine Learning July 24, 2026 By Alessandro Licciardi, Roberta Raineri, Anton Proskurnikov, Lamberto Rondoni, Lorenzo Zino

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions

Read the original on arXiv Machine Learning →

arXiv:2506. 02897v3 Announce Type: replace Abstract: Federated Learning (FL) enables privacy-preserving collaborative model training, but its effectiveness is often limited by client data heterogeneity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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.

By Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik

efficiency benchmarks

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

arXiv Machine Learning

Jun 25

FedReLa: Imbalanced Federated Learning via Re-Labeling

arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.

By Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng

safety

arXiv Machine Learning

Aug 4

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.

By Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen

safety

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 AI

Jun 30

Federated Learning with Energy-Based Structured Probabilistic Inference

arXiv:2606. 30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model.

By Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich

Related stories

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

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

FedReLa: Imbalanced Federated Learning via Re-Labeling

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

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

Federated Learning with Energy-Based Structured Probabilistic Inference