arXiv:2606. 10774v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information.
By Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya
arXiv:2607. 04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks.
By Zubaida Fatima, Zubair Shaban, Yusuf Jamal, Nazreen Shah, Ranjitha Prasad, B. N. Bharath
arXiv:2608. 09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL).
By Van Truong Vo, Khoa Nguyen, Taehong Kim
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
By Ferdous Pervej, Minseok Choi, Andreas F. Molisch
arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).
By Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He
Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs.
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
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
By Majid Kundroo, Tinku Singh, Taehong Kim
arXiv:2505. 09854v3 Announce Type: replace Abstract: As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge.
By Harikrishna Kuttivelil, Katia Obraczka
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.
By Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio, J\'anos Kert\'esz, M\'arton Karsai
arXiv:2608. 06946v1 Announce Type: cross Abstract: In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models.
By Fabien Mathieu (NPA), Alexandre Pham (NPA), Maria Gradinariu Potop-Butucaru (NPA), S{\'e}bastien Tixeuil (IUF, NPA)
arXiv:2606. 31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.
By Mohamad Mestoukirdi, Vincent Corlay