The paper investigates how scaling the number of clients in decentralized learning affects information propagation, consensus, and accuracy. By comparing a degree‑two Ring, a Static Random graph, and a locally adaptive Local‑First Heuristic Evolution (LFHE) topology on CIFAR‑10, the authors show that the Ring’s spectral gap decays as Θ(N⁻²), leading to slower convergence and higher disagreement. Experiments reveal that keeping local dataset size constant mitigates population penalties, and that LFHE can improve accuracy and consensus at the cost of increased communication.
By Yin-Kuan Liang, Yan Gao, Yang Long
The paper introduces connectivity-aware client selection for decentralized federated learning, using spectral graph tools to choose a subset of structurally relevant neighbors for each client. By leveraging eigenvector embeddings and heat/diffusion kernel similarities, the method reduces communication overhead while maintaining or improving accuracy compared to uniform subsampling and full broadcasting. Experiments show up to a 3‑percentage‑point accuracy gain under strong data heterogeneity and a 50% reduction in transmitted messages.
By Usevalad Milasheuski, Monica Nicoli, Stefano Savazzi
arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.
By Su Wang, Mung Chiang, H. Vincent Poor
arXiv:2607. 04254v1 Announce Type: cross Abstract: Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmission slot, significantly reducing communication latency.
By Kaushal Attaluri, Rebeca P. Diaz-Redondo, Manuel Fernandez Veiga
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:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.
By Ahmed Sayeed Faruk, Mohammad Shahverdikondori, Elena Zheleva
arXiv:2504. 17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers.
By Yacine Belal, Mohamed Maouche, Sonia Ben Mokhtar
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
The paper introduces D-FROST, an optimal‑transport‑based algorithm for decentralized federated prompt‑tuning, addressing challenges of heterogeneous local data and non‑IID distributions. It formulates prompt tuning as a Wasserstein optimization over prompt measures and merges neighborhood prompts into compact representative sets. The authors provide theoretical guarantees on consensus error and convergence, and demonstrate effectiveness through experiments on heterogeneous client data.
By Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang, My T. Thai
arXiv:2609.23773v1 Announce Type: cross
Abstract: Decentralised federated learning replaces server aggregation with peer-to-peer model exchange, making collaborator selection a local decision under u...
By Ke Xiao, Qiyuan Wang, Christos Anagnostopoulos
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
arXiv:2609.26063v1 Announce Type: new
Abstract: Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a pro...
By Yinlin Zhu, Di Wu, Wang Luo, Guocong Quan, Miao Hu