arXiv AI July 7, 2026 By Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio, J\'anos Kert\'esz, M\'arton Karsai

Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities

arXiv:2607. 03171v1 Announce Type: cross Abstract: Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, communication-efficient training process with no risk of single-point failure.

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

arXiv Machine Learning

Jul 7

Decentralized Federated Learning by Partial Message Exchange

arXiv:2603. 01730v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks.

By Shan Sha, Shenglong Zhou, Xin Wang, Lingchen Kong, Geoffrey Ye Li

safety

arXiv AI

Jun 11

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

arXiv:2606. 11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity.

By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni

benchmarks safety

arXiv AI

Jun 16

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.

By Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan, Guy Nagels

safety

arXiv Machine Learning

Jul 7

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.

By Liyang Yuan, Yibo Yang, Dandan Guo

benchmarks

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 AI

Jun 30

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv:2606. 28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data.

By Samuele Sabella, Chiara Boldrini, Lorenzo Valerio, Marco Conti, Andrea Passarella

efficiency

Related stories

Decentralized Federated Learning by Partial Message Exchange

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

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

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints