Energy-Efficient Split Learning for Resource-Constrained Environments: A Smart Farming Solution
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918134447865856 |
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| author | Soltani, Keiwan Tanwar, Vishesh Kumar Gupta, Ashish Das, Sajal K. |
| author_facet | Soltani, Keiwan Tanwar, Vishesh Kumar Gupta, Ashish Das, Sajal K. |
| contents | Smart farming systems encounter significant challenges, including limited resources, the need for data privacy, and poor connectivity in rural areas. To address these issues, we present eEnergy-Split, an energy-efficient framework that utilizes split learning (SL) to enable collaborative model training without direct data sharing or heavy computation on edge devices. By distributing the model between edge devices and a central server, eEnergy-Split reduces on-device energy usage by up to 86 percent compared to federated learning (FL) while safeguarding data privacy. Moreover, SL improves classification accuracy by up to 6.2 percent over FL on ResNet-18 and by more modest amounts on GoogleNet and MobileNetV2. We propose an optimal edge deployment algorithm and a UAV trajectory planning strategy that solves the Traveling Salesman Problem (TSP) exactly to minimize flight cost and extend and maximize communication rounds. Comprehensive evaluations on agricultural pest datasets reveal that eEnergy-Split lowers UAV energy consumption compared to baseline methods and boosts overall accuracy by up to 17 percent. Notably, the energy efficiency of SL is shown to be model-dependent-yielding substantial savings in lightweight models like MobileNet, while communication and memory overheads may reduce efficiency gains in deeper networks. These results highlight the potential of combining SL with energy-aware design to deliver a scalable, privacy-preserving solution for resource-constrained smart farming environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-Efficient Split Learning for Resource-Constrained Environments: A Smart Farming Solution Soltani, Keiwan Tanwar, Vishesh Kumar Gupta, Ashish Das, Sajal K. Distributed, Parallel, and Cluster Computing Emerging Technologies Smart farming systems encounter significant challenges, including limited resources, the need for data privacy, and poor connectivity in rural areas. To address these issues, we present eEnergy-Split, an energy-efficient framework that utilizes split learning (SL) to enable collaborative model training without direct data sharing or heavy computation on edge devices. By distributing the model between edge devices and a central server, eEnergy-Split reduces on-device energy usage by up to 86 percent compared to federated learning (FL) while safeguarding data privacy. Moreover, SL improves classification accuracy by up to 6.2 percent over FL on ResNet-18 and by more modest amounts on GoogleNet and MobileNetV2. We propose an optimal edge deployment algorithm and a UAV trajectory planning strategy that solves the Traveling Salesman Problem (TSP) exactly to minimize flight cost and extend and maximize communication rounds. Comprehensive evaluations on agricultural pest datasets reveal that eEnergy-Split lowers UAV energy consumption compared to baseline methods and boosts overall accuracy by up to 17 percent. Notably, the energy efficiency of SL is shown to be model-dependent-yielding substantial savings in lightweight models like MobileNet, while communication and memory overheads may reduce efficiency gains in deeper networks. These results highlight the potential of combining SL with energy-aware design to deliver a scalable, privacy-preserving solution for resource-constrained smart farming environments. |
| title | Energy-Efficient Split Learning for Resource-Constrained Environments: A Smart Farming Solution |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies |
| url | https://arxiv.org/abs/2509.02549 |