In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data. In semantic segmentation, the Federated source Free Domain Adaptation (FFreeDA) setting is of particular interest, where clients undergo unsupervised training after supervised pretraining at the server side. While few recent works address FL for autonomous vehicles, intrinsic real-world challenges such as the presence of adverse weather conditions and the existence of different autonomous agents are still unexplored. To bridge this gap, we address both problems and introduce a new federated semantic segmentation setting where both car and drone clients co-exist and collaborate. Specifically, we propose a novel approach for this setting which exploits a batch-norm weather-aware strategy to dynamically adapt the model to the different weather conditions, while hyperbolic space prototypes are used to align the heterogeneous client representations. Finally, we introduce FLYAWARE, the first semantic segmentation dataset with adverse weather data for aerial vehicles.

翻译：在联邦学习（FL）中，多个客户端无需共享私有数据即可协作训练全局模型。在语义分割领域，联邦无源域适应（FFreeDA）设置尤其受到关注，其中客户端在服务器端完成有监督预训练后进行无监督训练。尽管近期少数研究探讨了自动驾驶车辆的联邦学习，但内在的现实世界挑战——如恶劣天气条件的存在以及不同自主智能体的共存——仍未得到探索。为填补这一空白，我们同时应对这两个问题，并提出一种新的联邦语义分割设置，其中汽车与无人机客户端共存并协作。具体而言，我们针对该设置提出一种创新方法，利用批量归一化天气感知策略动态调整模型以适应不同天气条件，同时采用双曲空间原型对齐异构客户端表征。最后，我们引入了FLYAWARE——首个包含恶劣天气数据的航空器语义分割数据集。