Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection

1 code implementation • 2 May 2024 • Ahmad Khalil, Tizian Dege, Pegah Golchin, Rostyslav Olshevskyi, Antonio Fernandez Anta, Tobias Meuser

In this paper, we introduce FedProx+LA, a novel FL method building upon the state-of-the-art FedProx and FedLA to tackle data heterogeneity, which is specifically tailored for vehicular networks.

Autonomous Driving Federated Learning +2