# Federated Learning of Low-Rank One-Shot Image Detection Models in Edge Devices with Scalable Accuracy and Compute Complexity

arXiv: https://arxiv.org/abs/2504.16515 · HTML: https://mlsanity.com/paper/2504.16515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods](https://mlsanity.com/paper/2212.10025/) (2022)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning](https://mlsanity.com/paper/2309.01289/) (2023)
- [Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning](https://mlsanity.com/paper/2210.01708/) (2022)
- [Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach](https://mlsanity.com/paper/2109.13666/) (2021)
- [Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation](https://mlsanity.com/paper/2401.06563/) (2024)
- [A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks](https://mlsanity.com/paper/2306.14237/) (2023)
- [Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning](https://mlsanity.com/paper/2308.07273/) (2023)
- [Sequential Compression Layers for Efficient Federated Learning in Foundational Models](https://mlsanity.com/paper/2412.07021/) (2024)

## Abstract (arXiv)

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.