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
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:2610.08176v1 Announce Type: new
Abstract: Decentralized learning is highly sensitive to communication topology under non-IID data. Adaptive peer-selection methods can exploit local model inform...
By Yin-Kuan Liang (Durham University), Yan Gao (University of Cambridge), Yang Long (Durham University)
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.
By Alessandro Licciardi, Roberta Raineri, Anton Proskurnikov, Lamberto Rondoni, Lorenzo Zino
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
The paper presents a decentralized multitask learning framework that learns task relationships directly from distributed data. It introduces a two‑phase strategy: first estimating a generalized graph Laplacian from noisy stochastic gradient iterates, then using the learned graph to facilitate cooperative multitask diffusion learning. The authors provide theoretical analysis of Laplacian estimation error, its impact on steady‑state performance, and a topology sensitivity index, and confirm the benefits of learned task graphs through simulations.
By Zirui Wan, Stefan Vlaski