7 papers
Age of Information in Non-Terrestrial Networks with Energy Harvesting
Fangming Zhao, Nikolaos Pappas, Shi Jin +1
We analyze the timeliness of status-update delivery in a low Earth orbit (LEO) satellite-assisted energy-harvesting Internet of Things network using the Age of Information (AoI) me…
Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning
Eunjeong Jeong, Giovanni Perin, Howard H. Yang +1
Federated Learning (FL) on resource-constrained edge devices faces a critical challenge: The computational energy required for training Deep Neural Networks (DNNs) often dominates…
Rethinking Federated Learning Over the Air: The Blessing of Scaling Up
Jiaqi Zhu, Bikramjit Das, Yong Xie +2
Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communic…
Graph Attention Reinforcement Learning for Multicast Routing and Age-Optimal Scheduling
Yanning Zhang, Guocheng Liao, Shengbin Cao +3
Multicast routing is essential for real-time group applications, such as video streaming, virtual reality, and metaverse platforms, where the Age of Information (AoI) acts as a cru…
Age of Information in Random Access Networks with Energy Harvesting
Fangming Zhao, Nikolaos Pappas, Meng Zhang +1
We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capabilit…
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning
Eunjeong Jeong, Nikolaos Pappas
Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from compu…