Summary of Susfl: Energy-aware Federated Learning-based Monitoring For Sustainable Smart Farms, by Dian Chen et al.
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms
by Dian Chen, Paul Yang, Ing-Ray Chen, Dong Sam Ha, Jin-Hee Cho
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel federated learning-based system, SusFL, is proposed for sustainable smart farming. The system equips animals with solar sensors and Raspberry Pis to train local deep-learning models on health data. These sensors periodically update LoRa gateways, forming a wireless sensor network to detect diseases like mastitis. To optimize monitoring quality while minimizing energy use, mechanism design from game theory is incorporated, ensuring the system’s sustainability and resilience against attacks. Comparative analysis using real-time datasets demonstrates that SusFL outperforms existing methods in prediction accuracy, operational efficiency, system reliability, and social welfare maximization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SusFL is a new way to help farmers monitor animal health. It uses special sensors on animals and computers to train models and detect diseases like mastitis. The system is designed to use as little energy as possible while still being effective. To make sure the system works well, it uses game theory ideas to choose which sensors to use. This helps the system be more reliable and less vulnerable to attacks. By comparing SusFL to other methods, we showed that it’s better at predicting diseases, using less energy, and making sure the system keeps working. |
Keywords
* Artificial intelligence * Deep learning * Federated learning * Lora
