FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving

Researchers introduce FedS2R, a federated learning framework for semantic segmentation in autonomous driving that enables collaborative model training across multiple clients without sharing raw data. The system uses data augmentation and knowledge distillation to bridge the gap between synthetic training data and real-world driving scenarios, achieving near-parity performance with centralized training while maintaining privacy.

FedS2R addresses a critical challenge in autonomous vehicle development: training robust perception models across diverse datasets and conditions while preserving data privacy. The framework tackles two fundamental problems simultaneously—domain generalization (adapting synthetic data to real environments) and federated learning (collaborative training without centralizing sensitive data). This combination matters because autonomous driving companies operate in competitive, privacy-conscious environments where sharing raw sensor data across organizations remains impractical.

The advancement builds on years of federated learning progress in computer vision, but extends it specifically to semantic segmentation, which is computationally more intensive than image classification and essential for autonomous driving safety. Previous federated approaches focused primarily on classification tasks, leaving segmentation's unique demands largely unaddressed. The paper's emphasis on synthetic-to-real transfer reflects the industry reality that collecting large-scale labeled real-world driving datasets is expensive and time-consuming.

For the autonomous driving industry, FedS2R enables a new collaborative paradigm where companies can contribute diverse driving data to improve shared models without exposing proprietary datasets. The 2 mIoU point gap from centralized training suggests the approach achieves practical viability. This reduces barriers for smaller mobility companies to access high-quality perception models and accelerates development timelines.

Future directions likely include extending the framework to additional segmentation tasks (instance segmentation, panoptic segmentation) and exploring how performance scales with more heterogeneous client datasets and geographic diversity. The technique's effectiveness across five major datasets signals broader applicability across different driving conditions and regions.

• →FedS2R enables federated learning for semantic segmentation in autonomous driving while preserving data privacy across multiple organizations.
• →The framework closes the performance gap between centralized and federated training to within 2 mIoU points across five real-world datasets.
• →Inconsistency-driven data augmentation and multi-client knowledge distillation are the core technical innovations enabling one-shot federated domain generalization.
• →The approach reduces barriers for smaller autonomous vehicle companies to access and improve perception models without sharing proprietary sensor data.
• →Semantic segmentation remains underexplored in federated learning despite being critical for autonomous driving safety and decision-making.