4 papers
Federated Learning Meets Random Access: Energy-Efficient Uplink Resource Allocation
Giovanni Perin, Eunjeong Jeong, Nikolaos Pappas
Artificial intelligence-generated traffic is changing the shape of wireless networks. Specifically, as the amount of data generated to train machine learning models is massive, net…
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…
Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling
Eunjeong Jeong, Nikolaos Pappas
Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for l…
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…