  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.
