Federated Model Training

# Description Working group contributors can train various versions of the Reactive branch of AutoE2E on the [KITScenes dataset](https://huggingface.co/datasets/KIT-MRT/KITScenes-Multimodal). We should adopt a 90:10 train:val split since the data is relatively small. Contributors should share their training results and we can iteratively improve to achieve the best performance. ## Variations - Backbone: SwinV2Tiny, ConvNextV2Tiny, ResNet50 - Map Feature Fusion: Residual, Attention - Trajectory Planner: Bezier, Flow Matching Report key metrics: ADE (Average displacement error) and FDE (final displacement error) at 3s, 5s and the end of the trajectory which is at 6.4s ## Data Please also consider input data formats, e.g. applying noise to the input images or the map to make the network more robust. Please also consider map representation, rendering etc. we want to use navigation maps not Lanelet2 maps for now. ## Losses Please also consider loss function