Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity
This addresses the problem of efficient model deployment on heterogeneous edge devices for federated learning applications, representing an incremental improvement through integration of existing techniques.
The paper tackles the problem of training image detection models on resource-constrained edge devices by introducing LoRa-FL, a federated learning framework that combines low-rank adaptation with one-shot detection architectures. The result is competitive detection performance on MNIST and CIFAR10 datasets while significantly reducing communication bandwidth and compute complexity.
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.