This paper introduces a novel federated learning framework termed LoRa-FL designed for training low-rank one-shot image detection models deployed on edge devices. By incorporating low-rank adaptation techniques into one-shot detection architectures, our method significantly reduces both computational and communication overhead while maintaining scalable accuracy. The proposed framework leverages federated learning to collaboratively train lightweight image recognition models, enabling rapid adaptation and efficient deployment across heterogeneous, resource-constrained devices. Experimental evaluations on the MNIST and CIFAR10 benchmark datasets, both in an independent-and-identically-distributed (IID) and non-IID setting, demonstrate that our approach achieves competitive detection performance while significantly reducing communication bandwidth and compute complexity. This makes it a promising solution for adaptively reducing the communication and compute power overheads, while not sacrificing model accuracy.

本研究解决了在边缘设备上训练低秩一-shot图像检测模型所面临的计算和通信开销问题。提出的LoRa-FL框架通过结合低秩适应技术，显著降低了计算和通信成本，同时保持可扩展的准确性。实验结果表明，该方法在多个数据集上表现出竞争力的检测性能，表明其在资源受限的环境中具有广泛应用潜力。

边缘设备低秩一-shot图像检测模型的联邦学习：可扩展的准确性与计算复杂度