Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917081236111360 |
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| author | Jeong, Eunjeong Pappas, Nikolaos |
| author_facet | Jeong, Eunjeong Pappas, Nikolaos |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation Jeong, Eunjeong Pappas, Nikolaos Machine Learning Information Theory 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. |
| title | Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2511.11949 |