arXiv:2601. 00549v2 Announce Type: replace-cross Abstract: The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge intelligence.
By Zhiheng Guo, Zhaoyang Liu, Zihan Cen, Chenyuan Feng, Xinghua Sun, Xiang Chen, Tony Q. S. Quek, Xijun Wang
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. 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:2607. 29659v1 Announce Type: new Abstract: Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices.
By Idan Roth, Lutz Lampe
arXiv:2607. 13119v1 Announce Type: cross Abstract: In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration.
By Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
arXiv:2602. 02355v2 Announce Type: replace-cross Abstract: Hierarchical federated learning (HFL) is well suited for large-scale wireless and Internet of Things systems, where devices communicate with nearby edge servers before reaching the cloud.
By Amirreza Kazemi, Seyed Mohammad Azimi-Abarghouyi, Gabor Fodor, Carlo Fischione