Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. To address these hurdles, we navigate the uncharted waters of Semi-Supervised Federated Object Detection (SSFOD). We present a pioneering SSFOD framework, designed for scenarios where labeled data reside only at the server while clients possess unlabeled data. Notably, our method represents the inaugural implementation of SSFOD for clients with 0% labeled non-IID data, a stark contrast to previous studies that maintain some subset of labels at each client. We propose FedSTO, a two-stage strategy encompassing Selective Training followed by Orthogonally enhanced full-parameter training, to effectively address data shift (e.g. weather conditions) between server and clients. Our contributions include selectively refining the backbone of the detector to avert overfitting, orthogonality regularization to boost representation divergence, and local EMA-driven pseudo label assignment to yield high-quality pseudo labels. Extensive validation on prominent autonomous driving datasets (BDD100K, Cityscapes, and SODA10M) attests to the efficacy of our approach, demonstrating state-of-the-art results. Remarkably, FedSTO, using just 20-30% of labels, performs nearly as well as fully-supervised centralized training methods.

翻译：联邦学习作为一种在分布式数据源上训练模型同时保护数据隐私的强大框架已崭露头角。然而，它面临高质量标签有限和客户端数据非独立同分布的挑战，尤其在自动驾驶等应用中。为应对这些难题，我们探索了半监督联邦目标检测这一未知领域。我们提出了一种开创性的半监督联邦目标检测框架，专门设计用于标签数据仅存在于服务器端而客户端拥有无标签数据的场景。值得注意的是，我们的方法是首个针对客户端拥有0%标签非独立同分布数据的半监督联邦目标检测实现，这与先前研究在每个客户端保留部分标签子集的做法形成鲜明对比。我们提出了FedSTO——一种包含选择性训练和正交增强全参数训练的两阶段策略，以有效应对服务器与客户端之间的数据偏移（如天气条件）。我们的贡献包括：选择性精炼检测器的骨干网络以避免过拟合、采用正交正则化提升表示差异度，以及基于局部指数移动平均的伪标签分配机制以生成高质量伪标签。在主流自动驾驶数据集（BDD100K、Cityscapes和SODA10M）上的大量验证证明了我们方法的有效性，展现了最先进的性能。尤为突出的是，仅使用20-30%标签的FedSTO，其表现几乎与全监督集中式训练方法相当。