# FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding

arXiv: https://arxiv.org/abs/2407.01103 · HTML: https://mlsanity.com/paper/2407.01103/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2024.

## Built on by (most cited first, 8 of 8)

- [Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving](https://mlsanity.com/paper/2409.19560/) (2024)
- [Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory](https://mlsanity.com/paper/2501.01710/) (2025)
- [FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving](https://mlsanity.com/paper/2505.00318/) (2025)
- [Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator](https://mlsanity.com/paper/2502.02972/) (2025)
- [HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation](https://mlsanity.com/paper/2508.05135/) (2025)
- [FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving](https://mlsanity.com/paper/2512.06676/) (2025)
- [OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization](https://mlsanity.com/paper/2605.20276/) (2026)
- [AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence](https://mlsanity.com/paper/2607.16562/) (2026)

## Abstract (arXiv)

Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles' surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance.