{"ID":2841969,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11949","arxiv_id":"2511.11949","title":"Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation","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.","short_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 devic...","url_abs":"https://arxiv.org/abs/2511.11949","url_pdf":"https://arxiv.org/pdf/2511.11949v1","authors":"[\"Eunjeong Jeong\",\"Nikolaos Pappas\"]","published":"2025-11-14T23:46:48Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IT\"]","methods":"[]","has_code":false}