Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions
The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural sys...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/9/934 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850279226893139968 |
|---|---|
| author | Sai Puppala Koushik Sinha |
| author_facet | Sai Puppala Koushik Sinha |
| author_sort | Sai Puppala |
| collection | DOAJ |
| description | The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector. |
| format | Article |
| id | doaj-art-5fe211e3aa474d0b9da85aedb94ea26a |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-5fe211e3aa474d0b9da85aedb94ea26a2025-08-20T01:49:09ZengMDPI AGAgriculture2077-04722025-04-0115993410.3390/agriculture15090934Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network ConditionsSai Puppala0Koushik Sinha1Computer Science Department, Southern Illinois University, 1230 Lincoln Dr, Carbondale, IL 62901, USAComputer Science Department, Southern Illinois University, 1230 Lincoln Dr, Carbondale, IL 62901, USAThe advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector.https://www.mdpi.com/2077-0472/15/9/934smart farmingheterogeneous federated learningdynamic networksartificial intelligencewireless communications |
| spellingShingle | Sai Puppala Koushik Sinha Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions Agriculture smart farming heterogeneous federated learning dynamic networks artificial intelligence wireless communications |
| title | Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions |
| title_full | Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions |
| title_fullStr | Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions |
| title_full_unstemmed | Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions |
| title_short | Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions |
| title_sort | towards secure and efficient farming using self regulating heterogeneous federated learning in dynamic network conditions |
| topic | smart farming heterogeneous federated learning dynamic networks artificial intelligence wireless communications |
| url | https://www.mdpi.com/2077-0472/15/9/934 |
| work_keys_str_mv | AT saipuppala towardssecureandefficientfarmingusingselfregulatingheterogeneousfederatedlearningindynamicnetworkconditions AT koushiksinha towardssecureandefficientfarmingusingselfregulatingheterogeneousfederatedlearningindynamicnetworkconditions |