VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture
This provides a privacy-preserving and efficient solution for agricultural applications, but it is incremental as it combines existing methods like vision-language models and federated learning for a specific domain.
The paper tackles the problem of training object detection models in smart agriculture with privacy concerns and high communication costs by proposing VLLFL, a vision-language model-based lightweight federated learning framework, which achieves a 14.53% performance improvement and reduces communication overhead by 99.3%.
In modern smart agriculture, object detection plays a crucial role by enabling automation, precision farming, and monitoring of resources. From identifying crop health and pest infestations to optimizing harvesting processes, accurate object detection enhances both productivity and sustainability. However, training object detection models often requires large-scale data collection and raises privacy concerns, particularly when sensitive agricultural data is distributed across farms. To address these challenges, we propose VLLFL, a vision-language model-based lightweight federated learning framework (VLLFL). It harnesses the generalization and context-aware detection capabilities of the vision-language model (VLM) and leverages the privacy-preserving nature of federated learning. By training a compact prompt generator to boost the performance of the VLM deployed across different farms, VLLFL preserves privacy while reducing communication overhead. Experimental results demonstrate that VLLFL achieves 14.53% improvement in the performance of VLM while reducing 99.3% communication overhead. Spanning tasks from identifying a wide variety of fruits to detecting harmful animals in agriculture, the proposed framework offers an efficient, scalable, and privacy-preserving solution specifically tailored to agricultural applications.