Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection T aehyeon Kim 1 Eric Lin

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.