3 papers
eess.SP2025
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…
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.NI2024
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…