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cs.LG2025
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…
cs.LG2024
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems
Sravan Reddy Chintareddy, Keenan Roach, Kenny Cheung +1
In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the seconda…