Federated Learning for Robust Computer Vision in Intelligent Transportation Systems

We study Federated Learning (FL) as the promising technology to design the computer vision Machine Learning-based applications, which are robust to possible Data Quality (DQ) degradation in real Intelligent Transportation Systems (ITSs). As ITSs are composed of diverse data generation and communication units, DQ of images they produce and use may vary dramatically. We study the images of various quality distributed over the local clients and perform iterative FL training with two distinct aggregation strategies: Federated Averaging (FedAvg) and Geometric Median (GM). We consequently evaluate the models cross-trained over a single DQ cohort against other DQ categories. Then we analyze the image (traffic signs) recognition performance and its robustness toward the DQ degradation for each training cohort and aggregation strategy. Based on the results, we provide our recommendations on how to train more robust FL-based computer vision applications for ITSs.