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Federated Learning (FL) has emerged as a potent framework for training modelsacross 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 wherelabeled data reside only at the server while clients possess unlabeled data.Notably, our method represents the inaugural implementation of SSFOD forclients with 0% labeled non-IID data, a stark contrast to previous studies thatmaintain some subset of labels at each client. We propose FedSTO, a two-stagestrategy encompassing Selective Training followed by Orthogonally enhancedfull-parameter training, to effectively address data shift (e.g. weatherconditions) between server and clients. Our contributions include selectivelyrefining the backbone of the detector to avert overfitting, orthogonalityregularization to boost representation divergence, and local EMA-driven pseudolabel assignment to yield high-quality pseudo labels. Extensive validation onprominent autonomous driving datasets (BDD100K, Cityscapes, and SODA10M)attests to the efficacy of our approach, demonstrating state-of-the-artresults. Remarkably, FedSTO, using just 20-30% of labels, performs nearly aswell as fully-supervised centralized training methods.

Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection