Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resembles of federated learning algorithm like Federated averaging (FED Avg) or Federated SGD (FED SGD) to ensemble learning algorithms has 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.

翻译：联邦学习作为一种隐私保护算法已受到广泛关注，但其底层机制（如联邦平均算法或联邦随机梯度下降算法）与集成学习算法的相似性尚未得到充分探索。本文旨在探究联邦学习作为提升泛化能力的方法在目标检测中的应用，并将其性能与集中式训练方法进行对比。具体而言，我们研究了跨多个客户端使用联邦学习训练的YOLOv5模型，并采用无放回随机采样策略，使每个客户端持有与集中式训练相同数据集的部分样本。实验结果表明，联邦学习目标检测器的全局模型在对未见目标生成精确边界框方面展现出更优效率，其测试集包含来自两个不同客户端且未在训练数据集中出现的混合目标。这些发现表明，联邦学习可从集成学习视角理解，类似于Bagging与Boosting技术的协同融合。因此，联邦学习不仅可被视为一种增强隐私保护的方法，更可作为一种提升机器学习模型性能的手段。