A V2X-based Privacy Preserving Federated Measuring and Learning System
Levente Alekszejenkó Tadeusz Dobrowiecki
Future autonomous vehicles (AVs) will use a variety of sensors that generate
a vast amount of data. Naturally, this data not only serves self-driving
algorithms; but can also assist other vehicles or the infrastructure in
real-time decision-making. Consequently, vehicles shall exchange their
measurement data over Vehicle-to-Everything (V2X) technologies. Moreover,
predicting the state of the road network might be beneficial too. With such a
prediction, we might mitigate road congestion, balance parking lot usage, or
optimize the traffic flow. That would decrease transportation costs as well as
reduce its environmental impact.
  In this paper, we propose a federated measurement and learning system that
provides real-time data to fellow vehicles over Vehicle-to-Vehicle (V2V)
communication while also operating a federated learning (FL) scheme over the
Vehicle-to-Network (V2N) link to create a predictive model of the
transportation network. As we are yet to have real-world AV data, we model it
with a non-IID (independent and identically distributed) dataset to evaluate
the capabilities of the proposed system in terms of performance and privacy.
Results indicate that the proposed FL scheme improves learning performance and
prevents eavesdropping at the aggregator server side.