When Cars meet Drones: Hyperbolic Federated Learning for Source-Free
  Domain Adaptation in Adverse Weather
Giulia Rizzoli Matteo Caligiuri Donald Shenaj Francesco Barbato Pietro Zanuttigh
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.