arXiv:2610.38970v2 Announce Type: replace
Abstract: Current and emerging mobile networks face increasing privacy, scalability, and performance challenges as distributed learning becomes essential for...
By Martin Isaksson, Rickard C\"oster, \v{S}ar\=unas Girdzijauskas, Seif Haridi
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman
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
StoCFL is a clustered federated learning framework designed to address Non-IID data and dynamic client participation. It introduces a flexible clustering mechanism that allows arbitrary client participation and accommodates newly joined clients, improving data efficiency and model performance. Experiments on four Non-IID settings and a real-world dataset demonstrate that StoCFL achieves promising cluster results even when the number of clusters is unknown, outperforming baseline approaches across various scenarios.
By Dun Zeng, Xiangjing Hu, Shiyu Liu, Yue Yu, Qifan Wang, Zenglin Xu
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:2609.14246v1 Announce Type: cross
Abstract: In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further af...
By Changheng Wang, Xianchao Zhang, Zhiqing Wei, Lingzhu Zhao, Zhongming Yang, Zhiyong Feng