Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation

2025/11/14 by Jeong, Eunjeong, Pappas, Nikolaos · 1 citation
#FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2511.11949

Abstract

Federated Learning (FL) is a powerful paradigm for distributed learning, but its increasing complexity leads to significant energy consumption from client-side computations for training models. In particular, the challenge is critical in energy-harvesting FL (EHFL) systems where participation availability of each device oscillates due to limited energy. To address this, we propose FedBacys, a battery-aware EHFL framework using cyclic client participation based on users' battery levels. By clustering clients and scheduling them sequentially, FedBacys minimizes redundant computations, reduces system-wide energy usage, and improves learning stability. We also introduce FedBacys-Odd, a more energy-efficient variant that allows clients to participate selectively, further reducing energy costs without compromising performance. We provide a convergence analysis for our framework and demonstrate its superior energy efficiency and robustness compared to existing algorithms through numerical experiments.

Citations

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Cited by

• Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning

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