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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…