4 papers
Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks
Masoud Ghazikor, Zhou Ni, Morteza Hashemi
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…
Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems
Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan +1
In this paper, we propose an Expectation-Maximization-based (EM) Personalized Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Co…
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data
Zhou Ni, Masoud Ghazikor, Morteza Hashemi
Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address thi…
Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks
Ziru Chen, Zhou Ni, Peiyuan Guan +4
Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. Howe…