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.

翻译：联邦学习（FL）作为一种隐私保护算法已获得广泛关注，但联邦平均（FedAvg）或联邦SGD（FedSGD）等联邦学习算法与集成学习算法之间的内在关联尚未被充分探索。本文旨在研究将FL应用于目标检测以提升泛化性能的方法，并将其性能与集中式训练方法进行对比。具体而言，我们探究了采用跨多客户端联邦学习训练的YOLOv5模型性能，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所用数据集相同子集。实验结果表明，联邦学习目标检测器的全局模型在生成未见目标精确边界框方面展现出卓越效率，其测试集由训练数据集中未涉及的两个不同客户端的混合目标构成。这些发现表明，FL可从集成算法视角进行解读，类似于Bagging与Boosting技术的协同融合。因此，FL不仅可被视为增强隐私保护的手段，更能作为提升机器学习模型性能的方法。