arXiv Machine Learning By Masoud Ghazikor, Zhou Ni, Morteza Hashemi

Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks

Read the original on arXiv Machine Learning →

arXiv:2608. 00855v1 Announce Type: cross Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 7

Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

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