# Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm

arXiv: https://arxiv.org/abs/2306.17829 · HTML: https://mlsanity.com/paper/2306.17829/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Application of federated learning in manufacturing](https://mlsanity.com/paper/2208.04664/) (2022)

## Built on by (most cited first, 4 of 4)

- [Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques](https://mlsanity.com/paper/2408.08974/) (2024)
- [FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2408.04442/) (2024)
- [Federated Action Recognition for Smart Worker Assistance Using FastPose](https://mlsanity.com/paper/2508.14113/) (2025)
- [Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing](https://mlsanity.com/paper/2408.10024/) (2024)

## Abstract (arXiv)

Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms have not been fully explored. The purpose of this paper is to examine the application of FL to object detection as a method to enhance generalizability, and to compare its performance against a centralized training approach for an object detection algorithm. Specifically, we investigate the performance of a YOLOv5 model trained using FL across multiple clients and employ a random sampling strategy without replacement, so each client holds a portion of the same dataset used for centralized training. Our experimental results showcase the superior efficiency of the FL object detector's global model in generating accurate bounding boxes for unseen objects, with the test set being a mixture of objects from two distinct clients not represented in the training dataset. These findings suggest that FL can be viewed from an ensemble algorithm perspective, akin to a synergistic blend of Bagging and Boosting techniques. As a result, FL can be seen not only as a method to enhance privacy, but also as a method to enhance the performance of a machine learning model.